1.
20IT501 – Data Warehousing and
Data Mining
III Year / V Semester

2.
UNIT V - ADVANCED TRENDS
Time Series and Sequence Data in Transactional
Databases - Sentiment analysis - Descriptive Mining
of Complex Data Objects - Spatial Databases -
Multimedia Databases - Text mining - World Wide
Web - Applications and Trends in Data Mining -
Case studies involving classification and clustering

3.
Mining Time-Series Data
 A time-series database consists of sequences of values
or events obtained over repeated measurements of time.
 The values are typically measured at equal time
intervals (e.g., hourly, daily, weekly).
 Time-series databases are popular in many
applications, such as stock market analysis, economic
and sales forecasting and etc.

4.
Mining Time-Series Data
With the growing deployment of a large number
of sensors, telemetry devices, and other on-line
data collection tools, the amount of time-series
data is increasing rapidly, often in the order of
gigabytes per day (such as in stock trading) or
even per minute (such as from NASA space
programs).

5.
Mining Time-Series Data
Trend Analysis
 There are two goals in time-series analysis:
modeling time series (i.e., to gain insight into the
mechanisms or underlying forces that generate the time
series), and
forecasting time series (i.e., to predict the future values
of the time-series variables).

6.
Mining Time-Series Data
Trend Analysis – Components:
 Trend or long-term movements: These indicate the
general direction in which a time series graph is
moving over a long interval of time. This
movement is displayed by a trend curve, or a trend
line.

7.
Mining Time-Series Data
Trend Analysis – Components:
 Cyclic movements or cyclic variations: These
refer to the cycles, that is, the long-term
oscillations about a trend line or curve, which may
or may not be periodic.

8.
Mining Time-Series Data
 Trend Analysis – Components:
 Seasonal movements or seasonal variations: These are
systematic or calendar related.
 Examples include events that recur annually, such as the sudden
increase in sales of chocolates and flowers before Valentine’s
Day or of department store items before Christmas.
 The observed increase in water consumption in summer due to
warm weather is another example.

9.
Mining Time-Series Data
Trend Analysis – Components:
 Irregular or random movements: These
characterize the sporadic motion of time series due
to random or chance events, such as labor disputes,
floods, or announced personnel changes within
companies.

10.
Mining Time-Series Data
 Trend Analysis:
 Regression analysis has been a popular tool for modeling time
series, finding trends and outliers in such data sets.
 Time-series modeling is also referred to as the decomposition of
a time series into these four basic movements.
 The time-series variable Y can be modeled as either the product
of the four variables (i.e., Y = T x C x S x I) or their sum.

11.
Mining Time-Series Data
Trend Analysis:
 A common method for determining trend is to
calculate a moving average of order n as the
following sequence of arithmetic means:

12.
Mining Time-Series Data
Trend Analysis:
 A moving average tends to reduce the amount of
variation present in the data set.
Thus the process of replacing the time series by its
moving average eliminates unwanted fluctuations and
is therefore also referred to as the smoothing of time
series.

13.
Mining Time-Series Data
 Trend Analysis:
 Time-series forecasting finds a mathematical formula that will
approximately generate the historical patterns in a time series. It
is used to make long-term or short term predictions of future
values.
 There are several models for forecasting: ARIMA (Auto-
Regressive Integrated Moving Average), also known as the Box-
Jenkins methodology (after its creators), is a popular example.

14.
Mining Time-Series Data
Similarity Search in Time-Series Analysis
 Unlike normal database queries, which find data that
match the given query exactly, a similarity search finds
data sequences that differ only slightly from the given
query sequence.
 Given a set of time-series sequences, S, there are two
types of similarity searches: subsequence matching
and whole sequence matching.

15.
Mining Time-Series Data
Similarity Search in Time-Series Analysis
 Subsequence matching finds the sequences in S
that contain subsequences that are similar to a
given query sequence x, while whole sequence
matching finds a set of sequences in S that are
similar to each other (as a whole).

16.
Mining Time-Series Data
 Data Reduction and Transformation
Techniques:
 Attribute subset selection
 Dimensionality Reduction
 Numerosity Reduction

17.
Mining Time-Series Data
 Data Reduction and Transformation Techniques:
 Several dimensionality reduction techniques can be used in
time-series analysis.
 Examples include (1) the discrete Fourier transform (DFT) as the
classical data reduction technique, (2) more recently developed
discrete wavelet transforms (DWT), (3) Singular Value
Decomposition (SVD) based on Principle Components Analysis
(PCA) and (4) random projection-based sketch techniques

18.
Mining Time-Series Data
 Indexing Methods for Similarity Search
 For efficient accessing, a multidimensional index can
be constructed using the first few Fourier coefficients.
 When a similarity query is submitted to the system,
the index can be used to retrieve the sequences that are
at most a certain small distance away from the query
sequence.

19.
Mining Time-Series Data
 Indexing Methods for Similarity Search
 Postprocessing is then performed by computing
the actual distance between sequences in the time
domain and discarding any false matches.

20.
Mining Time-Series Data
 Indexing Methods for Similarity Search
 For subsequence matching, each sequence can be broken down into a
set of “pieces” of windows with length w.
 In one approach, the features of the subsequence inside each window
are then extracted.
 Each sequence is mapped to a “trail” in the feature space. The trail of
each sequence is divided into “subtrails,” each represented by a
minimum bounding rectangle.
 A multipiece assembly algorithm can then be used to search for longer
sequence matches.

21.
Mining Time-Series Data
Query Languages for Time Sequences
 A time-sequence query language should be able to
specify not only simple similarity queries like “Find all
of the sequences similar to a given subsequence Q,”
but also sophisticated queries like “Find all of the
sequences that are similar to some sequence in class
C1, but not similar to any sequence in class C2.”

22.
Mining Time-Series Data
 Query Languages for Time Sequences
 An interesting kind of time-sequence query language is a shape
definition language.
 It allows users to define and query the overall shape of time
sequences using human-readable series of sequence transitions
or macros, while ignoring the specific details.
 Such a shape definition language increases the users’ flexibility
at specifying queries of desired shapes for sequence similarity
search

23.
Mining Sequence Patterns in
Transactional Databases
 A sequence database consists of sequences of ordered
elements or events, recorded with or without a concrete
notion of time.
 Typical examples include customer shopping
sequences, Web clickstreams, biological sequences,
sequences of events in science and engineering, and in
natural and social developments.

24.
Mining Sequence Patterns in
Transactional Databases
 Sequential pattern mining is the mining of frequently
occurring ordered events or subsequences as patterns.
 An example of a sequential pattern is “Customers who
buy a Canon digital camera are likely to buy an HP
color printer within a month.”
 For retail data, sequential patterns are useful for shelf
placement and promotions.

25.
Mining Sequence Patterns in
Transactional Databases
 Given a set of sequences, where each sequence
consists of a list of events (or elements) and each event
consists of a set of items, and given a user-specified
minimum support threshold of min sup, sequential
pattern mining finds all frequent subsequences, that is,
the subsequences whose occurrence frequency in the
set of sequences is no less than min sup.”

26.
Mining Sequence Patterns in
Transactional Databases
 This model of sequential pattern mining is an
abstraction of customer-shopping sequence
analysis.

27.
Mining Sequence Patterns in
Transactional Databases
 Scalable Methods for Mining Sequential
Patterns:
 GSP adopts a candidate generate-and-test approach
using horizonal data format (where the data are
represented as < sequence ID : sequence of itemsets>,
as usual, where each itemset is an event).

28.
Mining Sequence Patterns in
Transactional Databases
 Scalable Methods for Mining Sequential Patterns:
 SPADE adopts a candidate generate and- test approach using
vertical data format (where the data are represented as <itemset :
(sequence ID, event ID)>).
 The vertical data format can be obtained by transforming from a
horizontally formatted sequence database in just one scan.
 PrefixSpan is a pattern growth method, which does not require
candidate generation.

29.
Mining Sequence Patterns in
Transactional Databases
 GSP: Generalized Sequential Patterns
 It is an extension of their seminal algorithm for
frequent itemset mining, known as Apriori.
 GSP uses the downward-closure property of
sequential patterns and adopts a multiple-pass,
candidate generate-and-test approach.

30.
Mining Sequence Patterns in
Transactional Databases
 GSP: Generalized Sequential Patterns
 In the first scan of the database, it finds all of the frequent
items, that is, those with minimum support.
 Each such item yields a 1-event frequent sequence
consisting of that item.
Each subsequent pass starts with a seed set of sequential
patterns—the set of sequential patterns found in the
previous pass.

31.
Mining Sequence Patterns in
Transactional Databases
 GSP: Generalized Sequential Patterns
 This seed set is used to generate new potentially
frequent patterns, called candidate sequences.
Each candidate sequence contains one more item than
the seed sequential pattern from which it was
generated (where each event in the pattern may contain
one or multiple items).

32.
Mining Sequence Patterns in
Transactional Databases
 GSP: Generalized Sequential Patterns
 It reduces search space, it typically needs to scan the
database multiple times.
 It will likely generate a huge set of candidate
sequences, especially when mining long sequences.
 There is a need for more efficient mining methods.

33.
Mining Sequence Patterns in
Transactional Databases
 SPADE:(Sequential PAttern Discovery using
Equivalent classes)
 It is an Apriori-based sequential pattern mining
algorithm that uses vertical data format.
 As with GSP, SPADE requires one scan to find the
frequent 1-sequences.

34.
Mining Sequence Patterns in
Transactional Databases
 SPADE:(Sequential PAttern Discovery using
Equivalent classes)
 To find candidate 2-sequences, we join all pairs of
single items if they are frequent (therein, it applies the
Apriori property), if they share the same sequence
identifier, and if their event identifiers follow a
sequential ordering

35.
Mining Sequence Patterns in
Transactional Databases
 SPADE:(Sequential PAttern Discovery using
Equivalent classes)
 The first item in the pair must occur as an event
before the second item, where both occur in the same
sequence.
Similarly, we can grow the length of itemsets from
length 2 to length 3, and so on

36.
Mining Sequence Patterns in
Transactional Databases
 SPADE:(Sequential PAttern Discovery using
Equivalent classes)
 The use of vertical data format, with the creation of ID
lists, reduces scans of the sequence database.
 The ID lists carry the information necessary to find the
support of candidates. As the length of a frequent sequence
increases, the size of its ID list decreases, resulting in very
fast joins.

37.
Mining Sequence Patterns in
Transactional Databases
 Mining Multidimensional, Multilevel Sequential
Patterns
 Sequence identifiers (representing individual customers,
for example) and sequence items (such as products bought)
are often associated with additional pieces of information.
 Mining multidimensional, multilevel sequential patterns is
the discovery of interesting patterns in such a broad
dimensional space, at different levels of detail.

38.
Mining Sequence Patterns in
Transactional Databases
 Constraint-Based Mining of Sequential
Patterns:
 we promote constraint-based mining, which
incorporates user-specified constraints to reduce
the search space and derive only patterns that are
of interest to the user.

39.
Mining Sequence Patterns in
Transactional Databases
 Constraint-Based Mining of Sequential Patterns:
 First, constraints can be related to the duration, T, of a
sequence.
The duration may be the maximal or minimal length of the
sequence in the database, or a user-specified duration
related to time, such as the year 2005.
Sequential pattern mining can then be confined to the data
within the specified duration, T.

40.
Mining Sequence Patterns in
Transactional Databases
 Constraint-Based Mining of Sequential Patterns:
 Constraints related to a specific duration, such as a
particular year, are considered succinct constraints.
 A constraint is succinct if we can enumerate all and only
those sequences that are guaranteed to satisfy the
constraint, even before support counting begins.
 Suppose, here, T = 2005. All of the sequences guaranteed
to satisfy the constraint before mining begins.

41.
Mining Sequence Patterns in
Transactional Databases
 Constraint-Based Mining of Sequential Patterns:
 Finally, a user can specify constraints on the kinds of
sequential patterns by providing “pattern templates”.
 A serial episode is a set of events that occurs in a total
order, whereas a parallel episode is a set of events
whose occurrence ordering is insignificant.

42.
Mining Sequence Patterns in
Transactional Databases
 Periodicity Analysis for Time-Related Sequence Data:
 Periodicity analysis is the mining of periodic patterns, that
is, the search for recurring patterns in time-related sequence
data.
Periodicity analysis can be applied to many important
areas. For example, seasons, tides, planet trajectories, daily
power consumptions, daily traffic patterns, and weekly TV
programs all present certain periodic patterns.

43.
Mining Sequence Patterns in
Transactional Databases
 Periodicity Analysis for Time-Related Sequence
Data:
 A full periodic pattern is a pattern where every point
in time contributes (precisely or approximately) to the
cyclic behavior of a time-related sequence.
For example, all of the days in the year approximately
contribute to the season cycle of the year.

44.
Mining Sequence Patterns in
Transactional Databases
 Periodicity Analysis for Time-Related Sequence Data:
 A partial periodic pattern specifies the periodic behavior of
a time-related sequence at some but not all of the points in
time.
 For example, Sandy reads the New York Times from 7:00
to 7:30 every weekday morning, but her activities at other
times do not have much regularity.

45.
Sentiment Analysis
 Sentiment analysis (or opinion mining) is a natural
language processing (NLP) technique used to determine
whether data is positive, negative or neutral.
 Sentiment analysis is often performed on textual data
to help businesses monitor brand and product sentiment
in customer feedback, and understand customer needs.

46.
Sentiment Analysis
 Sentiment analysis is the use of natural
language processing, text analysis,
computational linguistics, and biometrics to
systematically identify, extract, quantify, and
study affective states and subjective
information.

47.
Sentiment Analysis
 A basic task in sentiment analysis is classifying the
polarity of a given text at the document, sentence, or
feature/aspect level—whether the expressed opinion in
a document, a sentence or an entity feature/aspect is
positive, negative, or neutral. Advanced, "beyond
polarity" sentiment classification looks, for instance, at
emotional states such as enjoyment, anger, disgust,
sadness, fear, and surprise.

48.
Sentiment Analysis

49.
Sentiment Analysis
 A different method for determining sentiment is
the use of a scaling system whereby words
commonly associated with having a negative,
neutral, or positive sentiment with them are given
an associated number on a −10 to +10 scale (most
negative up to most positive) or simply from 0 to
a positive upper limit such as +4.

50.
Sentiment Analysis
 Subjectivity/objectivity identification:
 This task is commonly defined as classifying a given
text (usually a sentence) into one of two classes:
objective or subjective.
 The subjectivity of words and phrases may depend on
their context and an objective document may contain
subjective sentences (e.g., a news article quoting
people's opinions).

51.
Sentiment Analysis
 Subjectivity/objectivity identification:
 Subjective and objective identification, emerging
subtasks of sentiment analysis to use syntactic,
semantic features, and machine learning knowledge to
identify a sentence or document are facts or opinions.
 The term objective refers to the incident carry factual
information.

52.
Sentiment Analysis
 Subjectivity/objectivity identification:
 Example of an objective sentence:
“To be elected president of the India, a candidate must
be at least thirty-five years of age.”
 The term subjective describes the incident contains
non-factual information in various forms, such as
personal opinions, judgment, and predictions.

53.
Sentiment Analysis
 Subjectivity/objectivity identification:
 Example of a subjective sentence:
'We Indians need to elect a Prime Minister who is
mature and who is able to make wise decisions.'

54.
Descriptive Mining of
Complex Data Objects
 Application requirements have motivated the
design and development of object-relational
and object-oriented database systems.
These systems organize a large set of complex
data objects into classes, which are in turn
organized into class/subclass hierarchies.

55.
Descriptive Mining of
Complex Data Objects
 Each object in a class is associated with
 an object-identifier
 a set of attributes that may contain sophisticated
data structures
 a set of methods

56.
Descriptive Mining of
Complex Data Objects
 Generalization of Structured Data
 An important feature of object-relational and
object-oriented databases is their capability of
storing, accessing, and modeling complex
structure-valued data, such as set- and list-valued
data and data with nested structures.

57.
Descriptive Mining of
Complex Data Objects
 Generalization of Structured Data
 Generalization of each value in the set to its
corresponding higher-level concept, or
 Derivation of the general behavior of the set,

58.
Descriptive Mining of
Complex Data Objects
 Generalization of Structured Data
 A set-valued attribute may be generalized to a set-
valued or a single-valued attribute;
 A single-valued attribute may be generalized to a set-
valued attribute if the values form a lattice or
“hierarchy” or if the generalization follows different
paths.

59.
Descriptive Mining of
Complex Data Objects
 Generalization of Structured Data
 List-valued attributes and sequence-valued
attributes can be generalized in a manner similar to
that for set-valued attributes except that the order
of the elements in the list or sequence should be
preserved in the generalization.

60.
Descriptive Mining of
Complex Data Objects
 Generalization of Structured Data
 A complex structure-valued attribute may contain
sets, tuples, lists, trees, records, and their
combinations, where one structure may be nested
in another at any level.

61.
Descriptive Mining of
Complex Data Objects
 Generalization of Structured Data
 In general, a structure-valued attribute can be generalized
in several ways, such as
 generalizing each attribute in the structure while maintaining the
shape of the structure
 flattening the structure and generalizing the flattened structure,
 summarizing the low-level structures by high-level concepts or
aggregation
 returning the type or an overview of the structure

62.
Descriptive Mining of
Complex Data Objects
 Aggregation and Approximation in Spatial and
Multimedia Data Generalization
 Aggregation and approximation are another important
means of generalization.
 They are especially useful for generalizing attributes
with large sets of values, complex structures, and
spatial or multimedia data.

63.
Descriptive Mining of
Complex Data Objects
 Aggregation and Approximation in Spatial and
Multimedia Data Generalization
 Let’s take spatial data as an example.
We would like to generalize detailed geographic points
into clustered regions, such as business, residential,
industrial, or agricultural areas, according to land
usage.

64.
Descriptive Mining of
Complex Data Objects
 Aggregation and Approximation in Spatial and
Multimedia Data Generalization
 Spatial operators, such as spatial-union, spatial-
overlapping, and spatial-intersection (which may
require the merging of scattered small regions into
large, clustered regions) can also use spatial
aggregation and approximation as data generalization
operators.

65.
Descriptive Mining of
Complex Data Objects
 Aggregation and Approximation in Spatial and Multimedia
Data Generalization
 A multimedia database may contain complex texts, graphics,
images, video fragments, maps, voice, music, and other forms of
audio/video information.
 Multimedia data are typically stored as sequences of bytes with
variable lengths, and segments of data are linked together or
indexed in a multidimensional way for easy reference.

66.
Descriptive Mining of
Complex Data Objects
 Aggregation and Approximation in Spatial and Multimedia
Data Generalization
 Generalization on multimedia data can be performed by
recognition and extraction of the essential features and/or
general patterns of such data.
 For an image, the size, color, shape, texture, orientation, and
relative positions and structures of the contained objects or
regions in the image can be extracted by aggregation and/or
approximation.

67.
Descriptive Mining of
Complex Data Objects
 Generalization of Object Identifiers and Class/Subclass Hierarchies
 First, the object identifier is generalized to the identifier of the lowest
subclass to which the object belongs.
 The identifier of this subclass can then, in turn, be generalized to a
higher level class/subclass identifier by climbing up the class/subclass
hierarchy.
 A class or a subclass can be generalized to its corresponding
superclass(es) by climbing up its associated class/subclass hierarchy.

68.
Descriptive Mining of
Complex Data Objects
 Generalization of Object Identifiers and Class/Subclass
Hierarchies
 Some object-oriented database systems allow multiple
inheritance, where properties can be inherited from more
than one superclass when the class/subclass “hierarchy” is
organized in the shape of a lattice.
 The inherited properties of an object can be derived by
query processing in the object-oriented database.

69.
Descriptive Mining of
Complex Data Objects
 Generalization of Object Identifiers and Class/Subclass
Hierarchies
 Methods are an important component of object-oriented
databases.
 They can also be inherited by objects.
 A method is usually defined by a computational
procedure/function or by a set of deduction rules, it is
impossible to perform generalization on the method itself.

70.
Descriptive Mining of
Complex Data Objects
 Generalization of Class Composition Hierarchies:
 An attribute of an object may be composed of or
described by another object, some of whose attributes
may be in turn composed of or described by other
objects, thus forming a class composition hierarchy.
 Generalization on a class composition hierarchy can
be viewed as generalization on a set of nested
structured data

71.
Descriptive Mining of
Complex Data Objects
Construction and Mining of Object Cubes
 The generalization of multidimensional attributes
of a complex object class can be performed by
examining each attribute (or dimension),
generalizing each attribute to simple-valued data,
and constructing a multidimensional data cube,
called an object cube.

72.
Descriptive Mining of
Complex Data Objects
Construction and Mining of Object Cubes
 Once an object cube is constructed, multidimensional
analysis and data mining can be performed on it in a
manner similar to that for relational data cubes.
 Consider the attribute keyword, which may contain a
set of keywords describing a book.

73.
Descriptive Mining of
Complex Data Objects
 Generalization-Based Mining of Plan Databases by Divide-
and-Conquer
 A plan consists of a variable sequence of actions. A plan
database, or simply a planbase, is a large collection of plans.
 Plan mining can be used to discover travel patterns of business
passengers in an air flight database or to find significant patterns
from the sequences of actions in the repair of automobiles.

74.
Descriptive Mining of
Complex Data Objects
Generalization-Based Mining of Plan
Databases by Divide-and-Conquer

75.
Descriptive Mining of
Complex Data Objects
Generalization-Based Mining of Plan
Databases by Divide-and-Conquer

76.
Descriptive Mining of
Complex Data Objects
Generalization-Based Mining of Plan
Databases by Divide-and-Conquer

77.
Descriptive Mining of
Complex Data Objects
Generalization-Based Mining of Plan Databases
by Divide-and-Conquer
 A divide-and-conquer strategy, which first finds
interesting, high-level concise sequences of plans by
multidimensional generalization of a planbase, and
then partitions the planbase based on mined patterns to
discover the corresponding characteristics of
subplanbases.

78.
Spatial Data Mining
 A spatial database stores a large amount of space-related
data, such as maps, preprocessed remote sensing or medical
imaging data, and VLSI chip layout data.
 Spatial data mining refers to the extraction of knowledge,
spatial relationships, or other interesting patterns not
explicitly stored in spatial databases.
 Such mining demands an integration of data mining with
spatial database technologies.

79.
Spatial Data Mining
 It can be used for understanding spatial data,
discovering spatial relationships and
relationships between spatial and nonspatial
data, constructing spatial knowledge bases,
reorganizing spatial databases, and optimizing
spatial queries.

80.
Spatial Data Mining
 A crucial challenge to spatial data mining is the
exploration of efficient spatial data mining
techniques due to the huge amount of spatial data
and the complexity of spatial data types and
spatial access methods.
 The term geostatistics is often associated with
continuous geographic space.

81.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP
 A spatial data warehouse is a subject-oriented,
integrated, time-variant, and nonvolatile collection of
both spatial and nonspatial data in support of spatial
data mining and spatial-datarelated decision-making
processes.

82.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP
 The first challenge is the integration of spatial data
from heterogeneous sources and systems.
 The second challenge is the realization of fast and
flexible on-line analytical processing in spatial data
warehouses.

83.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP – Types of Data:
 A nonspatial dimension - “hot” for temperature and
“wet” for precipitation
 A spatial-to-nonspatial dimension is a dimension
 A spatial-to-spatial dimension - regions covering 0-5
degrees (Celsius), 5-10 degrees, and so on.

84.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP – Types of Measure:
 Numerical measure: only numerical data. For
example, one measure in a spatial data warehouse
could be the monthly revenue of a region, so that a
roll-up may compute the total revenue by year, by
county, and so on.

85.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP – Types of Measure:
 Spatial measure: A collection of pointers to spatial
objects. The regions with the same range of
temperature and precipitation will be grouped into the
same cell, and the measure so formed contains a
collection of pointers to those regions

86.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP – Types of Measure:
 Of the three measures, area and count are numerical
measures that can be computed similarly as for
nonspatial data cubes; region map is a spatial measure
that represents a collection of spatial pointers to the
corresponding regions.

87.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP

88.
Spatial Data Mining
Spatial Data Cube Construction and Spatial
OLAP – Types of Measure:
 region name dimension: probe location < district
< city < region
time dimension: hour < day < month < season <
province

89.
Spatial Data Mining
 Spatial Data Cube Construction and Spatial OLAP – Types of Measure:
 temperature dimension:
 (cold, mild, hot)  all(temperature)
 (below 􀀀20, 􀀀20...􀀀11, 􀀀10...0)  cold
 (0...10, 11...15, 16...20)  mild
 (20...25, 26...30, 31...35, above 35)  hot
 precipitation dimension:
 (dry, fair, wet) all(precipitation)
 (0...0.05, 0.06...0.2)  dry
 (0.2...0.5, 0.6...1.0, 1.1...1.5)  fair
 (1.5...2.0, 2.1...3.0, 3.1...5.0, above 5.0)  wet

90.
Spatial Data Mining
 Spatial Data Cube Construction and Spatial OLAP – Types
of Measure:
 Collect and store the corresponding spatial object pointers but
do not perform precomputation of spatial measures in the spatial
data cube.
 Precompute and store a rough approximation of the spatial
measures in the spatial data cube.
 Selectively precompute some spatial measures in the spatial data
cube.

91.
Spatial Data Mining
 Mining Spatial Association and Co-location Patterns:
 A spatial association rule is of the form AB [s%,c%],
where A and B are sets of spatial or nonspatial predicates,
s% is the support of the rule, and c%is the confidence of
the rule.
 Example: is a(X,“school”)^close_to(X, “sports_ center”)
 close_to(X,“park”) [0:5%;80%].

92.
Spatial Data Mining
 Spatial Classification and Spatial Trend
Analysis
 Spatial classification analyzes spatial objects to
derive classification schemes in relevance to
certain spatial properties, such as the neighborhood
of a district, highway, or river.

93.
Spatial Data Mining
 Spatial Classification and Spatial Trend Analysis
 Trend analysis detects changes with time, such as the
changes of temporal patterns in time-series data.
 Spatial trend analysis replaces time with space and
studies the trend of nonspatial or spatial data changing
with space.

94.
Multimedia Data Mining
 A multimedia database system stores and
manages a large collection of multimedia data,
such as audio, video, image, graphics, speech,
text, document, and hypertext data, which
contain text, text markups, and linkages.

95.
Multimedia Data Mining
 Similarity Search in Multimedia Data
 description-based retrieval systems, which build indices
and perform object retrieval based on image descriptions,
such as keywords, captions, size, and time of creation;
 content-based retrieval systems, which support retrieval
based on the image content, such as color histogram,
texture, pattern, image topology, and the shape of objects
and their layouts and locations within the image.

96.
Multimedia Data Mining
 Similarity Search in Multimedia Data –
Approaches:
 Color histogram–based signature: two images with
similar color composition but that contain very
different shapes or textures may be identified as
similar, although they could be completely unrelated
semantically.

97.
Multimedia Data Mining
 Similarity Search in Multimedia Data –
Approaches:
Multifeature composed signature: color histogram,
shape, image topology, and texture. The extracted
image features are stored as metadata, and images
are indexed based on such metadata.

98.
Multimedia Data Mining
 Similarity Search in Multimedia Data – Approaches:
 Wavelet-based signature: Wavelets capture shape, texture,
and image topology information in a single unified
framework.
 Wavelet-based signature with region-based granularity: the
computation and comparison of signatures are at the
granularity of regions, not the entire image.

99.
Multimedia Data Mining
 Multidimensional Analysis of Multimedia Data
 A multimedia data cube can contain additional
dimensions and measures for multimedia information,
such as color, texture, and shape.
 A multimedia data mining system prototype called
MultiMediaMiner, which extends the DBMiner system
by handling multimedia data.

100.
Multimedia Data Mining
 Multidimensional Analysis of Multimedia
Data
 Each image contains two descriptors: a feature
descriptor and a layout descriptor.
The original image is not stored directly in the
database; only its descriptors are stored.

101.
Multimedia Data Mining
 Multidimensional Analysis of Multimedia Data
 The description information encompasses fields like image file
name, image URL, image type (e.g., gif, tiff, jpeg, mpeg, bmp,
avi)
 The feature descriptor is a set of vectors for each visual
characteristic. The main vectors are a color vector containing the
color histogram quantized to 512 colors (8 8 8 for R G B), an
MFC(Most Frequent Color) vector, and an MFO(Most Frequent
Orientation) vector.

102.
Multimedia Data Mining
 Multidimensional Analysis of Multimedia Data
 The layout descriptor contains a color layout vector and an edge
layout vector. Regardless of their original size, all images are
assigned an 8 x 8 grid.
 The most frequent color for each of the 64 cells is stored in the
color layout vector, and the number of edges for each orientation
in each of the cells is stored in the edge layout vector.
 Other sizes of grids, like 4x4, 2x2, and 1x1, can easily be
derived.

103.
Multimedia Data Mining
 Multidimensional Analysis of Multimedia Data
 A multimedia data cube can have many dimensions:
 the size of the image or video in bytes; the width and height of the
frames (or pictures), constituting two dimensions; the date on
which the image or video was created (or last modified); the format
type of the image or video; the frame sequence duration in seconds;
the image or video Internet domain; the Internet domain of pages
referencing the image or video (parent URL); the keywords; a color
dimension; an edge-orientation dimension;

104.
Multimedia Data Mining
 Classification and Prediction Analysis of Multimedia Data
 Data preprocessing is important when mining image data and
can include data cleaning, data transformation, and feature
extraction.
 Aside from standard methods used in pattern recognition, such
as edge detection and Hough transformations, techniques can be
explored, such as the decomposition of images to eigenvectors or
the adoption of probabilistic models to deal with uncertainty.

105.
Multimedia Data Mining
 Mining Associations in Multimedia Data
 Association rules involving multimedia objects can be mined in
image and video databases.
 Associations between image content and nonimage content
features
 Associations among image contents that are not related to
spatial relationships
 Associations among image contents related to spatial
relationships

106.
Multimedia Data Mining
 Audio and Video Data Mining
 MPEG-k (developed by MPEG: Moving Picture Experts Group)
and JPEG are typical video compression schemes.
 The most recently released MPEG-7, formally named
“Multimedia Content Description Interface,” is a standard for
describing the multimedia content data.
 It supports some degree of interpretation of the information
meaning, which can be passed onto, or accessed by, a device or a
computer.

107.
Multimedia Data Mining
 Audio and Video Data Mining
 The audiovisual data description in MPEG-7
includes still pictures, video, graphics, audio,
speech, three-dimensional models, and information
about how these data elements are combined in the
multimedia presentation.

108.
Multimedia Data Mining
 Audio and Video Data Mining
 The MPEG committee standardizes the following
elements in MPEG-7:
 a set of descriptors - color, shape, texture
 a set of descriptor schemes - structure and semantics
 a set of coding schemes
 a description definition language (DDL)

109.
Multimedia Data Mining
 Audio and Video Data Mining
 To capture the story or event structure of a video, it is
better to treat each video clip as a collection of actions
 A shot is a group of frames or pictures
 The most representative frame in a video shot is
considered the key frame of the shot.
 The sequence of key frames will then be used to define the
sequence of the events happening in the video clip.

110.
Text Mining
 Text databases (or document databases),
which consist of large collections of
documents from various sources, such as news
articles, research papers, books, digital
libraries, e-mail messages, and Web pages.

111.
Text Mining
 Text Data Analysis and Information Retrieval
 Information retrieval is concerned with the
organization and retrieval of information from a large
number of text-based documents.
 Applications: On-line library catalog systems, on-line
document management systems, and the more recently
developed Web search engines.

112.
Text Mining
 Text Data Analysis and Information Retrieval
 To locate relevant documents in a document collection based on
a user’s query, which is often some keywords describing an
information need.
 A user takes the initiative to “pull” the relevant information out
from the collection (short-term)
 To “push” any newly arrived information item to a user if the
item is judged as being relevant to the user’s information need.
(Long term)

113.
Text Mining
 Basic Measures for Text Retrieval: Precision
and Recall:
 Precision: This is the percentage of retrieved
documents that are in fact relevant to the query
(i.e., “correct” responses). It is formally defined as

114.
Text Mining
 Basic Measures for Text Retrieval: Precision
and Recall:
 Recall: This is the percentage of documents that
are relevant to the query and were, in fact,
retrieved. It is formally defined as

115.
Text Mining
 Basic Measures for Text Retrieval: Precision and
Recall:
 An information retrieval system often needs to trade
off recall for precision or vice versa. One commonly
used trade-off is the F-score, which is defined as the
harmonic mean of recall and precision:

116.
Text Mining
 Basic Measures for Text Retrieval: Precision
and Recall:
 In order to measure the quality of a ranked list of
documents, it is common to compute an average of
precisions at all the ranks where a new relevant
document is returned.

117.
Text Mining
 Text Retrieval Methods - Boolean retrieval mode:
 Document is represented by a set of keywords and a user
provides a Boolean expression of keywords, such as “car and
repair shops,” “tea or coffee,” or “database systems but not
Oracle.”
 The retrieval system would take such a Boolean query and
return documents that satisfy the Boolean expression.
 Boolean retrieval method generally only works well when the
user knows a lot about the document collection

118.
Text Mining
 Text Retrieval Methods - Vector Space model
 represent a document and a query both as vectors in a
high-dimensional space corresponding to all the
keywords and use an appropriate similarity measure to
compute the similarity between the query vector and
the document vector.
 The similarity values can then be used for ranking
documents.

119.
Text Mining
 Text Retrieval Methods - Vector Space model
 The first step in most retrieval systems is to identify
keywords for representing documents, a preprocessing
step often called tokenization.
 A stop list is a set of words that are deemed
“irrelevant.” For example, a, the, of, for, with, and so
on are stop words, even though they may appear
frequently.

120.
Text Mining
 Text Retrieval Methods - Vector Space model
 A group of different words may share the same word
stem.
A text retrieval system needs to identify groups of
words where the words in a group are small syntactic
variants of one another and collect only the common
word stem per group.

121.
Text Mining
 Text Retrieval Methods - Vector Space model
 The term frequency be the number of occurrences
of term t in the document d, that is, freq(d, t).

122.
Text Mining
 Text Retrieval Methods - Vector Space model
 Inverse document frequency (IDF), that represents the
scaling factor, or the importance, of a term t.
 If a term t occurs in many documents, its importance will
be scaled down due to its reduced discriminative power.
 For example, the term database systems may likely be less
important if it occurs in many research papers in a database
system conference.

123.
Text Mining
 Text Retrieval Methods - Vector Space model
 In a complete vector-space model, TF and IDF are
combined together, which forms the TF-IDF
measure:
TF-IDF(d, t) = TF(d, t) x IDF(t)

124.
Text Mining
 Text Retrieval Methods - Vector Space model
 A representative metric is the cosine measure,
defined as follows.
Let v1 and v2 be two document vectors. Their
cosine similarity is defined as

125.
Text Mining
 Text Indexing Techniques - inverted index:
 An inverted index is an index structure that maintains
two hash indexed or B+-tree indexed tables: document
table and term table, where
 document table consists of a set of document records, each
containing two fields: doc id and posting list, where posting
list is a list of terms (or pointers to terms) that occur in the
document, sorted according to some relevance measure.

126.
Text Mining
 Text Indexing Techniques - inverted index:
 An inverted index is an index structure that maintains
two hash indexed or B+-tree indexed tables: document
table and term table, where
 term table consists of a set of term records, each containing
two fields: term id and posting list, where posting list
specifies a list of document identifiers in which the term
appears.

127.
Text Mining
 Text Indexing Techniques - signature file:
 A signature file is a file that stores a signature record for
each document in the database
 Each signature has a fixed size of b bits representing
terms.
Each bit of a document signature is initialized to 0. A bit is
set to 1 if the term it represents appears in the document.

128.
Text Mining
 Dimensionality Reduction for Text - Latent Semantic
Indexing
 One of the most popular algorithms for document
dimensionality reduction.
 It is fundamentally based on SVD (singular value
decomposition).
 The basic idea of LSI is to extract the most representative
features, and at the same time the reconstruction error can be
minimized.

129.
Text Mining
 Dimensionality Reduction for Text - Locality
Preserving Indexing
 To extract the most discriminative features.
 The basic idea of LPI is to preserve the locality
information (i.e., if two documents are near each other in
the original document space, LPI tries to keep these two
documents close together in the reduced dimensionality
space).

130.
Text Mining
 Dimensionality Reduction for Text - Locality
Preserving Indexing
 LSI aims to find the best subspace approximation to
the original document space in the sense of minimizing
the global reconstruction error.
 LPI aims to discover the local geometrical structure of
the document space

131.
Text Mining
 Text Mining Approaches
 the keyword-based approach, where the input is a set
of keywords or terms in the documents
 the tagging approach, where the input is a set of tags
 the information-extraction approach, which inputs
semantic information, such as events, facts, or entities
uncovered by information extraction

132.
Text Mining
 Text Mining Approaches - Keyword-Based Association
Analysis
 It collects sets of keywords or terms that occur frequently
together and then finds the association or correlation
relationships among them.
 Association analysis first preprocesses the text data by
parsing, stemming, removing stop words, and so on, and
then evokes association mining algorithms.

133.
Text Mining
 Text Mining Approaches - Keyword-Based Association
Analysis
 In a document database, each document can be viewed as a
transaction, while a set of keywords in the document can be
considered as a set of items in the transaction.
That is, the database is in the format
{document id, a set of keywords}

134.
Text Mining
 Text Mining Approaches - Keyword-Based
Association Analysis
 A set of frequently occurring consecutive or closely
located keywords may form a term or a phrase.
 Mining based on these associations is referred to as
“term-level association mining”.

135.
Text Mining
 Text Mining Approaches - Keyword-Based
Association Analysis
 terms and phrases are automatically tagged so there is
no need for human effort in tagging documents
 the number of meaningless results is greatly reduced,
as is the execution time of the mining algorithms.

136.
Text Mining
 Text Mining Approaches - Document Classification Analysis:
 To automatically organize such documents into classes to facilitate
document retrieval and subsequent analysis
 Document classification has been used in automated topic tagging (i.e.,
assigning labels to documents), topic directory construction,
identification of the document writing styles (which may help narrow
down the possible authors of anonymous documents), and classifying
the purposes of hyperlinks associated with a set of documents.

137.
Text Mining
 Text Mining Approaches - Document Classification
Analysis:
 First, a set of preclassified documents is taken as the training
set.
 The training set is then analyzed in order to derive a
classification scheme. Such a classification scheme often needs
to be refined with a testing process.
 The so-derived classification scheme can be used for
classification of other on-line documents.

138.
Text Mining
 Text Mining Approaches - Document Classification
Analysis:
 According to the vector-space model, two documents are
similar if they share similar document vectors.
This model motivates the construction of the k-nearest-
neighbor classifier, based on the intuition that similar
documents are expected to be assigned the same class label

139.
Text Mining
 Text Mining Approaches - Document Classification
Analysis:
 A feature selection process can be used to remove terms in
the training documents that are statistically uncorrelated
with the class labels.
This will reduce the set of terms to be used in
classification, thus improving both efficiency and accuracy.

140.
Text Mining
 Text Mining Approaches - Document
Classification Analysis:
 Association-based classification, which classifies
documents based on a set of associated, frequently
occurring text patterns

141.
Text Mining
 Text Mining Approaches - Document Clustering
Analysis
 The spectral clustering method first performs spectral
embedding (dimensionality reduction) on the original
data, and then applies the traditional clustering
algorithm (e.g., k-means) on the reduced document
space

142.
Text Mining
 Text Mining Approaches - Document Clustering
Analysis
 The mixture model clustering method models the text data
with a mixture model, often involving multinomial
component models.
Clustering involves two steps:
Estimating the model parameters based on the text data and any
additional prior knowledge
 inferring the clusters based on the estimated model parameters

143.
Mining the World Wide Web
