introduction to data mining

1. Knowledge Acquisition In
Decision Making (SQIT 3033)
Izwan Nizal Mohd Shaharanee
SQS 4017/ 6866
nizal@uum.edu.my
izwan.nizal@gmail.com

2. Course Objective
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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
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Intro to Knowledge Acquisition aka ~knowledge discovery~
(3 hours)
Knowledge Discovery Process (4 hours)
Pre-processing data (5 hours)
Predictive Modeling (10 hours)
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Evaluation And Implementation (6 hours)
Descriptive Modeling (7 hours)
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Decision Tree, Regression, Neural Network, Rough Set
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
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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-tharfiles-20120511-1yi3q.html

9. The Age of Big Data
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“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 ?
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Why?
Data explosion (tremendous amount of data available + cloud
computing)
 Data is being warehoused
 Computing power – Bionic Skin?
 Competitive pressure
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Hard Disk Nowadays more than 1TB capacities

11. What is Knowledge Acquisitions ?
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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 ( nontrivial, unknown data)
By automatic or semiautomatic means
It’s impossible to find pattern using manual method.

12. Traditional Approaches
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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
SQL
DBMS
DB

13. Disciplines Of Data Mining
Database System
Machine Learning
Algorithm
Statistics
Data Mining
Visualization
Information Retrieval

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

15. Try to related with your previous
knowledge?
Hmmm…how this data mining differ with
forecasting or prediction?
 Are there similar?
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16. Predictive Model
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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
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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
Profit (RM)
O ? Future data
x x x
x x x x x
x
xx x x x
x x
Current data
months

19. Descriptive Model
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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
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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
Results
y
o
y
y
y
y
y
y y
y
y
o
o
o
y y
o
o o o
y
o
Group 3
o o o
y
o
y
x x
o o
x
o
x x
x
x
x
Group 2
x x
Group 1
major

22. View Of DM
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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
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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
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Hypothesis Testing vs Knowledge Discovery
 Hypothesis
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Top down approach
Attempts to substantiate or disprove preconceived idea
 Knowledge
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Testing
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
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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
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Knowledge Discovery
 Directed
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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
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Knowledge Discovery
 Undirected
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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
Predict the future value
Data Mining
Predictive
Define R/S among data
Descriptive
•Classification
•Clustering
•Time
•Association
•Regression
Series Analysis
•Prediction
•Summarization
Rules
•Sequence Discovery

29. Knowledge Discovery Process

30. Knowledge Discovery Process

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

33. Knowledge Discovery Process
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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
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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::
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Data Reduction Data Cleaning, Data Integration,
Data Transformation, Data Reduction and Data
Discretization

35. Knowledge Discovery Process
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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), KMeans & K-Nearest Neighbor (clustering), Decision
Tree (classification), Instance Learning (classification).

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

38. Assignment 1
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Group Assignment >> you may be selected (randomly) to present your
answer? (2 minutes max)
Discuss how prediction/forecasting related to your life? Or any issues
related to prediction/forecasting that might interest to you.
You may discuss
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Give an appropriated example? Ect. Weather forecasting can determine your
daily exercise planning?
How it been done?
Minimum 1 pages
Due Date: 18 September 2013