introduction to data mining class with all the learning objectives and the evaluation

1. Knowledge Acquisition In
Decision Making (SQIT 3033)
Izwan Nizal Mohd Shaharanee
SQS 4017/ 6364
nizal@uum.edu.my
izwan.nizal@gmail.com

2. Course Objective
 To introduce :: knowledge about data mining
and data warehouse
 To evaluate and understand several data
mining techniques
 To enhance skill on data mining through
analysis problem in business
 Being able to apply the commonly used
functions of SAS Enterprise Miner and
WEKA to solve data mining problems
 Developing the skills of data mining modeling
and data analysis with SAS Enterprise Miner
and WEKA

3. Course Content
 Intro to Knowledge Acquisition aka ~knowledge discovery~
(3 hours)
 Knowledge Discovery Process (4 hours)
 Pre-processing data (5 hours)
 Predictive Modeling (10 hours)
 Decision Tree, Regression, Neural Network, Rough Set
 Evaluation And Implementation (6 hours)
 Descriptive Modeling (7 hours)
 Clustering, Association Rules
 Data mining ethics (1.5 hours)
 PROJECT PRESENTATION

4. Course Evaluation
 Assignments
 Case study + Presentations
 Project + Poster Presentations
 Mid Term ? Quizzes ?
 Class PARTICIPATION !!
 Final Exam 40%
60%

5. PreRequisites
 A “Basic statistics course such as
SQQS2023”Bussiness Statistical”+” programming
language knowledge”+“SAS knowledge”+”Database”+
“spreadsheet+ web 2.0”
 Passion in computer applications
 Dare to take the challenges
 Have a sincere heart to understand infinite God’s
knowledge
 Attendance is compulsory (no freely “tuang kelas”)
 Behave your “gadget". Please respect others

6. Timetable

7. Please introduce yourself..
http://padlet.com/wall/8yly4q2yu8
Facebook Group
https://www.facebook.com/dataharvester2.0
Youtube Channel + Vimeo Video
izwan nizal

8. http://www.theage.com.au/it-pro/business-it/data-miners-find-theres-gold-in-them-thar-
files-20120511-1yi3q.html

9. The Age of Big Data
 “The BBC documentary follows people who mine
Big Data, including LAPD police officers who use
data to predict crime, a London scientist/trader
who makes millions with math, and a South
African astronomer who wants to catalog the
entire cosmos.”
 “Data Scientist” is the sexiest job of the 21st
century. The Harvard Business Review made this
claim last October and it seems that everyone
(including your grandmother) has been repeating it
ever since.

10. Why Knowledge Acquisitions ?
 Why?
 Data explosion (tremendous amount of data available + cloud
computing)
 Data is being warehoused
 Computing power – Bionic Skin?
 Competitive pressure
Hard Disk Nowadays more than 1TB capacities

11. What is Knowledge Acquisitions ?
 aka :: data mining, knowledge discovery, knowledge
extraction, information discovery, information
harvesting ect.
 Process of discovering useful information,hidden
pattern or rules in large quantities of data ( non-
trivial, unknown data)
 By automatic or semiautomatic means
 It’s impossible to find pattern using manual method.

12. Traditional Approaches
 Traditional database queries:. Access a
database using a well defined query such as
SQL
 The query output consist of data from
database
 The output usually a subset of the database
DBMS
DB
SQL

13. Disciplines Of Data Mining
Data Mining
Information RetrievalAlgorithm
Machine Learning Visualization
StatisticsDatabase System

14. Data Mining Model & Task
Data Mining
Predictive Descriptive
•Classification
•Regression
•Time Series Analysis
•Prediction
•Clustering
•Summarization
•Association Rules
•Sequence Discovery

15. Try to related with your previous
knowledge?
 Hmmm…how this data mining differ with
forecasting or prediction?
 Are there similar?

16. Predictive Model
 Make prediction about values of data using
known results found from different data
 Or based on the use of other historical data
 Example:: credit card fraud, breast cancer
early warning, terrorist act, tsunami and ect.
 Ghost Protocol, Minority Report, Eagle Eye,

17. Predictive Model
 Perform inference on the current data to make
predictions.
 We know what to predict based on historical data)
 Never accurate 100%
 Concentrate more to input output relation ship ( x,f(x))
 Typical Question
 Which costumer are likely to buy this product next
four month
 What kind of transactions that are likely to be
fraudulent
 Who is likely to drop this paper?

18. Predictive Model
x
x x
xx
x
x
x
x
x
x
x x
x
x
x
months
Profit (RM)
Current data
Future dataO ?

19. Descriptive Model
 Identifies pattern or relationships in data.
 Serves as a way to explore the properties of data
examined, not to predict new properties
 Always required a domain expert
 Example::
 Segmenting marketing area
 Profiling student performances
 Profiling GooglePlay/ AppleApps customer

20. Descriptive Model
 Discovering new patterns inside the data
 We may don’t have any idea how the data looks like
 Explores the properties of the data examined
 Pattern at various granularities (eg: Student: University-
> faculty->program-> major?
 Typical Question
 What is the data
 What does it look like
 What does the data suggest for group of costumer
advertisement?

21. Descriptive Model
major
Results
x
x x
x
x
x
x
x
x
x
o
o
o
o
oo
o
o
o
o
o
o
o
o
o
o
y
y
y
y
y y
yy y
y
y
y y
y
y
Group 1
Group 2
Group 3

22. View Of DM
 Data To Be Mined
 Data warehouse, WWW, time series, textual. spatial
multimedia, transactional
 Knowledge To Be Mined
 Classification, prediction, summarization, trend
 Techniques Utilized
 Database, machine learning, visualization, statistics
 Applications Adapted
 Marketing, demographic segmentation, stock analysis

23. DM In Action
 Medical Applications ::clinical diagnosis, drug analysis
 Business (marketing segmentation & strategies, insolvency
predictor, loan risk assessment
 Education (Online learning)
 Internet (searching engine)
 Ect

24. Data Mining Methodology
 Hypothesis Testing vs Knowledge Discovery
 Hypothesis Testing
 Top down approach
 Attempts to substantiate or disprove preconceived idea
 Knowledge Discovery
 Bottom-up approach
 Start with data and tries to get it to tell us something
we didn’t already know

25. Data Mining Methodology
 Hypothesis Testing
 Generate good ideas
 Determine what data allow these hypotheses to be
tested
 Locate the data
 Prepare the data for analysis
 Build computer models based on the data
 Evaluate computer model to confirm or reject
hypotheses

26. Data Mining Methodology
 Knowledge Discovery
 Directed
 Identified sources of pre classified data
 Prepare data analysis
 Select appropriated KD techniques based on data
characteristics and data mining goal
 Divide data into training, testing and evaluation
 Use the training dataset to build model
 Tune the model by applying it to test dataset
 Take action based on data mining results
 Measure the effect of the action taken
 Restart the DM process taking advantage of new data
generated by the action taken

27. Data Mining Methodology
 Knowledge Discovery
 Undirected
 Identified available data sources
 Prepare data analysis
 Select appropriated undirected KD techniques based on
data characteristics and data mining goal
 Use the selected technique to uncover hidden structure in
the data
 Identify potential targets for directed KD
 Generate new hypothesis to test

28. Revision::
Two Approaches In data Mining
Data Mining
Predictive Descriptive
•Classification
•Regression
•Time Series Analysis
•Prediction
•Clustering
•Summarization
•Association Rules
•Sequence Discovery
Predict the future value Define R/S among data

29. Knowledge Discovery Process

30. Knowledge Discovery Process

31. Knowledge Discovery Process
 1.0 Selection
 The data needs for the data mining process may be
obtained from many different and heterogeneous
data sources
 Examples
 Business Transactions
 Scientific Data
 Video and pictures
 UUM Student Database

33. Knowledge Discovery Process
 2.0 Pre Processing
 Main idea – to ensure that data is clean (high quality of
data).
 The data to be used by the process may have
incorrect or missing data.
 There may be anomalous data from multiple
sources involving different data types and
metrics
 Erroneous data may be corrected or removed,
whereas missing data must be supplied or
predicted (Often using data mining tools)

34. Knowledge Discovery Process
 3.0 Transformation
 Data from different sources must be converted
into a common format for processing
 Some data may be encoded or transformed into
more usable formats
 Example::
 Data Reduction Data Cleaning, Data Integration,
Data Transformation, Data Reduction and Data
Discretization

35. Knowledge Discovery Process
 4.0 Data Mining
 Main idea –to use intelligent method to extract patterns
and knowledge from database
 This step applies algorithms to the transformed data to
generate the desired results.
 The heart of KD process (where unknown pattern will be
revealed).
 Example of algorithms: Regression (classification,
prediction), Neural Networks (prediction, classification,
clustering), Apriori Algorithms (association rules), K-
Means & K-Nearest Neighbor (clustering), Decision
Tree (classification), Instance Learning (classification).

36. Knowledge Discovery Process
 5.0 Interpretation/Evaluation
 How the data mining results are presented to the
users is extremely important because the
usefulness of the results is dependent on it
 Example::
 Graphical
 Geometric
 Icon Based
 Pixel Based
 Hierarchical Based
 Hybrid

37. Case Study: Predicting SQS Final
Year’s Student Performance
activities
Student
database
{contains
30,000 records}
Academics
academics
Selected record
{matric, PMK, grades} –
only 2,000 records
(contains incomplete
records etc.
academics
Clean record {replace
the missing value,
removed the replicated}
Y=w1x1+w2x2+b1
Generated Model :
pattern for
prediction
Testing result:
90 % correct 
accept model
Knowledge
(apply model)
Using neural
networks :
transform into
numerical.
Selection Pre-processing
Transformation
Data mining
Interpretation
& evaluation

38. Assignment 1
 Individual Assignment >> you may be selected (randomly) to present
your answer? (2 minutes max)
 Discuss how data mining application can be utilized in construction
project.
 You may discuss
 Give an appropriated example of any construction issues? Ect. Weather
forecasting to determine the development time, housing loan issues..
 How it can been done?
 At least 2 pages, 1.5 spacing (Do include your references and
proper citation)
 Due Date: 6 March 2014
 Title Page(need to supply with the due date)
 Upload your assignment through SCRIBD (you may need to
register) and share the documents with me
 No need to printout. Share the document. Save the planet. Go
green