Download Data mining

MIS 451
Building Business Intelligence Systems
Introduction to Data Mining
Why data mining?
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OLAP can only provide shallow data analysis -- what
 Ex: sales distribution by product
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Why data mining?
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Shallow data analysis is not sufficient to support
business decisions -- how

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Ex: how to boost sales of other products
Ex: when people buy product 6 what other products do
they are likely to buy? – cross selling
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Why data mining?

OLAP can only do shallow data analysis
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OLAP is based on SQL
SELECT PRODUCTS.PNAME, SUM(SALESFACTS.SALES_AMT)
FROM DBSR.PRODUCTS PRODUCTS, DBSR.SALESFACTS SALESFACTS
WHERE ( ( PRODUCTS.PRODUCT_KEY = SALESFACTS.PRODUCT_KEY ) )
GROUP BY PRODUCTS.PNAME;
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The nature of SQL decides that complicated algorithm cannot be
implemented with SQL.
Complicated algorithms need to be developed to support
deep data analysis – data mining
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Why data mining?

OLAP results generated from data sets with large number of attributes
are difficult to be interpreted

Ex: cluster customers of my company --- target marketing
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Pick two attributes related to a customer: income level and sales amount
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Why data mining?
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Ex: cluster customers of my company --- target marketing
Pick three attributes related to a customer: income level, education level
and sales amount
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What is data mining?

Data mining is a process to extract hidden and
interesting patterns from data.

Data mining is a step in the process of Knowledge
Discovery in Database (KDD).
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Steps of the KDD Process
Step 4:
Data Mining
Step 2:
Cleaning
Step 5:
Interpretation
& Evaluation
Knowledge
Step 3:
Transformation
Patterns
Step 1:
Selection
Transformed
Data
Preprocessed
Data
Data
Target Data
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Steps of the KDD Process
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Step 1: select interested columns (attributes) and
rows (records) to be mined.
Step 2: clean errors from selected data
Step 3: data are transformed to be suitable for high
performance data mining
Step 4: data mining
Step 5: filter out non-interesting patterns from data
mining results
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Data mining – on what kind of data

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Transactional Database
Data warehouse
Flat file
Web data
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Web content
Web structure
Web log
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Major data mining tasks
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Association rule mining – cross selling
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Clustering – target marketing
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Classification – potential customer identification,
fraud detection
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Reading : data mining book chapter 1
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