1. MIS 451 Building Business Intelligence Systems
Introduction to Data Mining

2. Why data mining? OLAP can only provide shallow data analysis -- what
Ex: sales distribution by product

3. Why data mining?
Shallow data analysis is not sufficient to support business decisions -- how
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

4. Why data mining? OLAP can only do shallow data analysis
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;
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

5. 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
Pick two attributes related to a customer: income level and sales amount

6. Why data mining?
Ex: cluster customers of my company --- target marketing
Pick three attributes related to a customer: income level, education level and sales amount

7. 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).

8. Steps of the KDD Process
Step 5: Interpretation & Evaluation
Step 4:
Data Mining
Knowledge
Patterns
Step 3: Transformation
Step 2: Cleaning
Transformed
Data
Preprocessed
Data
Step 1: Selection
Target Data
Data

9. Steps of the KDD Process
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

10. Data mining – on what kind of data
Transactional Database
Data warehouse
Flat file
Web data
Web content
Web structure
Web log

11. Major data mining tasks
Association rule mining – cross selling
Clustering – target marketing
Classification – potential customer identification, fraud detection

12. Reading : data mining book chapter 1