introduction to data mining

1. By
Dr.T.GopiKrishna
Assistant Professor
Dept of Computer Sciene and Engineering

2. What is Data Mining?
Extracting and ‘Mining’ knowledge from large amounts of data(Big data).
(Or)
Non-trivial extraction of implicit, previously unknown and potentially useful
information from data.
(or)
Exploration & analysis, by automatic or semi-automatic means, of large
quantities of data in order to discover meaningful patterns.
“Gold Mining from rock or sand” is same as “Knowledge mining from data”
Other terms for Data Mining:
o Knowledge Mining
o Knowledge Extraction
o Pattern Analysis
o Data Archaeology
Data Mining is not same as KDD (Knowledge Discovery from Data)
Data Mining is a step in KDD

3. Why Mine Data? Commercial Viewpoint

4. Why Mine Data? Scientific Viewpoint

5.  There is often information “hidden” in the data that is
not readily evident.
 Human analysts may take weeks to discover useful information.
 Much of the data is never analyzed at all
• Huge Volume of data
• Major Sources of Abundant data: - Business – Web, E-commerce,
Transactions, Stocks - Science – Remote Sensing, Bio informatics,
Scientific Simulation - Society and Everyone – News, Digital Cameras,
You Tube
• Need for turning data into knowledge – Drowning in data, but starving
for knowledge
• Applications that use data mining: - Market Analysis - Fraud
Detection - Customer Retention - Production Control - Scientific
Exploration
• Data rich and information poor situation

7. Machine Learning/
Pattern
Recognition
Statistics/
AI
Data Mining
Database
systems

8.  Mostly reads
 Queries are long and complex
 Gb - Tb of data
 History
 Lots of scans
 Summarized, reconciled data
 Hundreds of users (e.g., decision-
makers, analysts)
Data Warehouse:-Data spread in several databases – physically
located at numerous sites Data warehouse – repository of multiple
DBs in single schema; resides at single site.

9.  Machine learning is a field of artificial intelligence that uses
statistical techniques to give computer systems the ability to "learn"
 Machine learning explores the study and construction
of algorithms that can learn from and make predictions on data.
 Machine learning is closely related to (and often overlaps
with) computational statistics, which also focuses on prediction-
making through the use of computers.

10. “Machine Learning is the science of getting
computers to learn and act like humans do,
and improve their learning over time in
autonomous fashion, by feeding them data
and information in the form of observations
and real-world interactions.”

11.  Statistics – “Learning from Data” or “Turning data into
information”.
 Data – Crude Information – Does not makes sense – What we
capture & store
e.g. customer data, store data, demographical data,
geographical data
 Information – relates items of data – relevant to the decision
problem
e.g. X lives in Z; S is Y years old; X and S moved; W has money
in Z
 Facts – Information becomes facts when data can support it
 Knowledge – What we know or infer – relates items of information
e.g. a quantity Q of product A is used in region Z; customers of
class L use N% of C in period D

12.  Databases
 Data Warehousing
 Statistics
 Machine Learning
 Information Retrieval
 Image and Signal Processing
 Pattern Recognition
 Neural Networks
 Data Visualization
 Spatial / Temporal Data Analysis

13. Database-oriented data sets and applications
o Relational database, data warehouse, transactional database
Advanced data sets and advanced applications
o Data streams and sensor data
o Time-series data, temporal data, sequence data (incl. bio-
sequences)
o Structure data, graphs, social networks and multi-linked data
 Object-relational databases
o Heterogeneous databases and legacy databases
o Spatial data and spatiotemporal data
o Multimedia database o Text databases
o The World-Wide Web

14.  Prediction Methods
Use some variables to predict unknown or
future values of other variables.
 Description Methods
Find human-interpretable patterns that
describe the data.

15. Classification [Predictive]
Clustering [Descriptive]
Association Rule Discovery [Descriptive]
Sequential Pattern Discovery [Descriptive]
Regression [Predictive]
Deviation Detection [Predictive]

16.  Data mining uncovers this in-depth
business intelligence by using
advanced analytical and modelling
techniques.
 With data mining, you can ask far
more sophisticated questions of your
data than you can with conventional
querying methods.

17. Data mining is simply the acquisition of
information that is already present in your
CRM (Customer Relationship Management
System) that is intended to be utilized for
marketing, customer service, customer
informative services and similar
applications.

18.  Data mining tools ease and automate the process of
discovering this kind of information from large stores of data.
 Data mining can identify patterns in company data,
for example, in records of supermarket purchases.
If, for example, customers buy product A and product B,
which product C are they most likely to buy as well?
Accurate answers to questions like these are invaluable
aids to marketing strategies.
 Data mining can identify the characteristics of a known
group of customers, for example, those who have a proven
record as poor credit risks.

19.  Relational Databases:
Consists of Database (inter related data) and set of software programs to
manage and access data.
Collection of tables
Each table has a set of attributes (columns / fields) and large set of tuples
(records or rows) .
 Transactional Databases:
Consists of a file with records where each record is a transaction.
Each transaction has a unique transaction ID and list of items that make
up transactions.
 Object-Relational Databases:
 Temporal Databases, Sequence Databases and Time-Series Databases
 Spatial Databases and Spatiotemporal Databases:
 Text Databases and Multimedia Databases:
 Heterogeneous Databases and Legacy Databases:

20. Stages of Data Mining Process

21. KDD Process

22. Brief explanation of data mining stages

23. There are several major data
mining techniques have been developing and
using in data mining projects recently
including
 association,
 classification,
 clustering,
 prediction,
 sequential patterns and
 decision tree.

24. Data Mining Techniques(Association)
 Association is one of the best-known data mining technique. In
association, a pattern is discovered based on a relationship
between items in the same transaction.
 That’s is the reason why association technique is also known
as relation technique. The association technique is used in market
basket analysis to identify a set of products that customers
frequently purchase together.
 Retailers are using association technique to research customer’s
buying habits. Based on historical sale data, retailers might find
out that customers always buy crisps when they buy beers, and,
therefore, they can put beers and crisps next to each other to save
time for the customer and increase sales.

25. Classification
 Classification is a classic data mining technique based on
machine learning. Basically, classification is used to classify
each item in a set of data into one of a predefined set of
classes or groups.
 Classification method makes use of mathematical techniques
such as decision trees, linear programming, neural network,
and statistics.
 In classification, we develop the software that can learn how
to classify the data items into groups.
For example, we can apply classification in the
application that “given all records of employees who left
the company, predict who will probably leave the
company in a future period.”

26. Clustering
 Clustering is a data mining technique that makes a
meaningful or useful cluster of objects which have
similar characteristics using the automatic technique.
 The clustering technique defines the classes and puts
objects in each class, while in the classification
techniques, objects are assigned into predefined
classes.
 To make the concept clearer, we can take book
management in the library as an example. In a
library, there is a wide range of books on various
topics available.
 The challenge is how to keep those books in a way
that readers can take several books on a particular
topic without hassle.
 By using the clustering technique, we can keep books
that have some kinds of similarities in one cluster or
one shelf and label it with a meaningful name. If
readers want to grab books in that topic, they would
only have to go to that shelf instead of looking for the
entire library.

27. Prediction
 The prediction, as its name implied, is one of a data
mining techniques that discovers the relationship
between independent variables and relationship
between dependent and independent variables.
 For instance, the prediction analysis technique can
be used in the sale to predict profit for the future if
we consider the sale is an independent variable,
profit could be a dependent variable.
 Then based on the historical sale and profit data,
we can draw a fitted regression curve that is used
for profit prediction.

28. Sequential Patterns
 Sequential patterns analysis is one of data mining
technique that seeks to discover or identify similar
patterns, regular events or trends in transaction data
over a business period.
 In sales, with historical transaction data, businesses
can identify a set of items that customers buy
together different times in a year.
 Then businesses can use this information to
recommend customers buy it with better deals based
on their purchasing frequency in the past.

29. Decision trees
The A decision tree is one of the most commonly used data mining
techniques because its model is easy to understand for users.
In decision tree technique, the root of the decision tree is a simple
question or condition that has multiple answers.
Each answer then leads to a set of questions or conditions that help us
determine the data so that we can make the final decision based on it.
For example, We use the following decision tree to determine whether
or not to play tennis:

30. Knowledge Representation
Knowledge representation is the presentation of
knowledge to the user for visualization in terms
of trees, tables, rules graphs, charts, matrices,
etc.
For Example: Histograms

31. Histograms
•Histogram provides the representation of a distribution of
values of a single attribute.
•It consists of a set of rectangles, that reflects the counts
or frequencies of the classes present in the given data.
Example: Histogram of an electricity bill generated for 4
months, as shown in diagram given below.

32. Data Visualization
It deals with the representation of data in a
graphical or pictorial format.
Patterns in the data are marked easily by
using the data visualization technique.
Pixel- oriented visualization technique
In pixel based visualization techniques, there
are separate sub-windows for the value of
each attribute and it is represented by one
colored pixel.

33. Pixel- oriented visualization technique
•The color mapping of the
pixel is decided on the basis
of data characteristics and
visualization tasks.

34. Geometric projection visualization
technique
i. Scatter-plot matrices
It consists of scatter plots of all possible pairs of variables in a dataset.
ii. Hyper slice
It is an extension to scatter-plot matrices. They represent multi-
dimensional
function as a matrix of orthogonal two dimensional slices.
iii. Parallel co-ordinates T he parallel vertical lines which are separated
defines the axes.
A point in the Cartesian coordinates corresponds to a polyline in parallel
coordinates.
3. Icon-based visualization techniques
Icon-based visualization techniques are also known as iconic display
techniques.
Each multidimensional data item is mapped to an icon.
This technique allows visualization of large amount of data.
The most commonly used technique is Chernoff faces.

35. Chernoff faces
For example: The face width, the length of the mouth and the length of
nose, etc. as shown in the following diagram.

36. Visualization techniques
Hierarchical visualization techniques
 Hierarchical visualization techniques are
used for partitioning of all dimensions in to
subset.
 These subsets are visualized in
hierarchical manner.

37. Some of the visualization techniques are:
i. Dimensional stacking In dimension stacking,
n-dimensional attribute space is partitioned in
2-dimensional subspaces.
Attribute values are partitioned into various classes.
Each element is two dimensional space in the form of xy
plot.
Helps to mark the important attributes and are used on
the outer level.
ii. Mosaic plotMosaic plot gives the graphical
representation of successive decompositions.
Rectangles are used to represent the count of
categorical data and at every stage, rectangles are split
parallel.

38. Tree maps visualization
 Techniques are well suited for displaying large amount of
hierarchical structured data.
 The visualization space is divided into the multiple rectangles
that are ordered, according to a quantitative variable.
 The levels in the hierarchy are seen as rectangles containing
the other rectangle.
 Each set of rectangles on the same level in the hierarchy
represents a category, a column or an expression in a data set.
 Visualization complex data and relations
 This technique is used to visualize non-numeric data.
For example: text, pictures, blog entries and product reviews.

39. Expert systems
Rely on domain experts for decision making - using their knowledge intuition
o Time consuming, costly, error prone, biased
So the solution is to use Data Mining tools
– performs data analysis,
- finds data patterns

41. Knowledge Base:
Domain knowledge is used to guide search – used to evaluate
interestingness of patterns.
Includes concept hierarchies, user benefits, thresholds, metadata
Database / Data warehouse Server:
Responsible for fetching relevant data based on data mining
request.
Data Mining Engine:
Consists of modules for characterization, association, correlation analysis,
classification, cluster analysis, prediction, outlier analysis and evolution
analysis.
Pattern Evaluation Module:
Interacts with data mining modules. Focuses the search
towards interesting patterns.
Pattern evaluation module may be integrated with mining module
to confine the search.
User Interface:
Communicates between users and data mining system
Specifies data mining query – to focus search
Uses intermediate data mining results to perform exploratory

42. Major Issues in Data Mining:
Mining Methodology Issues:
o Mining different kinds of knowledge in databases.
o Incorporation of background knowledge
o Handling noisy or incomplete data
o Pattern Evaluation – Interestingness Problem
User Interaction Issues:
o Interactive mining of knowledge at multiple levels of abstraction
o Data mining query languages and ad-hoc data mining.
o Presentation and visualization of data mining results.
Performance Issues:
o Efficiency and Scalability of Data Mining Algorithms.
o Parallel, distributed and incremental mining algorithms.
Issues related to diversity of data types:
o Handling of relational and complex types of data.
o Mining information from heterogeneous databases and global I
nformation systems.

43. Review Questions
1. What motivated Data Mining? Why is it
important?
2. What is Data Mining?
3. Explain the steps in the Knowledge Discovery
Process.
4. Detail on the Architecture of Data Mining
Systems with a suitable diagram.
5. Explain about various Data Mining functionalities
6. Discuss about the major issues in data mining.