1. An overview of The IBM Intelligent Miner for Data By: Neeraja Rudrabhatla 11/04/1999

2. Mining Features supported by the Data Miner: Association Rules Clustering - Demographic, Neural networks Predicting classifications - Neural Networks, Decision Trees Predicting values Discovering sequential patterns Discovering similar time sequences

3. Steps for mining data using the Data Miner: Creation of data Analyze and prepare data for mining Mine the data using one or a combination of mining techniques Visualize mining results using advanced graphical techniques

4. Main Window of the Data Miner:

5. Database used for mining association rules: Store ID Customer # Date(yymmdd) Transaction # ItemID 001 0000007 950109 00982 122 001 0000007 950109 00982 125 001 0000007 950109 00982 133 001 0000007 950109 00982 150 001 0000003 950109 00983 153 001 0000003 950109 00983 154 001 0000003 950109 00983 162 001 0000003 950109 00983 166 001 0000005 950109 00984 147 001 0000005 950109 00984 174 001 0000005 950109 00984 191 001 0000005 950109 00984 198 001 0000008 950109 00985 147 001 0000008 950109 00985 174 001 0000008 950109 00985 182 001 0000008 950109 00985 184 001 0000006 950109 00986 174 001 0000006 950109 00986 186 001 0000006 950109 00986 187 001 0000006 950109 00986 188 001 0000002 950109 00987 109

6. Name Mapping:

7. Results of mining for associations:

8. Results on the automobile Database:

9. Another view:

10. Database used for Clustering: Gender Age Siblings Income Type Product female 18.02 1 97 red 2 female 13.03 6 490 green 3 male 11.0 3 647 red 4 female 47.5 2 3192 green 5 male 11.07 5 736 blue 6 female 24.0 3 22358 blue 7 female 62.1 0 3936 green8 female 04.08 1 516 pink 1 female 40.1 0 9478 red 2 female 04.08 0 193 pink3 female 45.8 5 16984 green 4 male 21.07 0 10428 blue 5 male 07.02 0 960 blue 6 female 42.5 0 10835 pink 7 female 36.9 2 37083 green 8 male 10.03 3 877 blue 1 male 02.03 0 10 blue 2 female 20.0 0 15432 green 3

11. Clustering - Demographic: Max #clusters: 9 Accuracy: 5% Max #clusters: 9 Accuracy: 5%

12. Details of Cluster 7:

13. Detailed pie-chart for attribute Type:

14. Detailed bar-graph of attribute Age:

15. Output obtained with Clustering using Neural Networks:

16. Details of Cluster 6:

17. Database used for Classification: Day Outlook Temperature Humidity Wind PlayTennis D1 Sunny Hot High Weak No D2 Sunny Hot High Strong No D3 Overcast Hot High Weak Yes D4 Rain Mild High Weak Yes D5 Rain Cool Normal Weak Yes D6 Rain Cool Normal Strong No D7 Overcast Cool Normal Strong Yes D8 Sunny Mild High Weak No D9 Sunny Cool Normal Weak Yes D10 Rain Mild Normal Weak Yes D11 Sunny Mild Normal Strong Yes D12 Overcast Mild High Strong Yes D13 Overcast Hot Normal Weak Yes D14 Rain Mild High Strong No

18. Classification using Decision Tree:

19. A view of a leaf node of the decision tree:

20. Classification using neural network: In-sample: 4 Out-Sample: 1 Accuracy: 80 Error: 10 Learning Rate: 0.1 Momentum: 0.9 In-sample: 4 Out-Sample: 1 Accuracy: 80 Error: 10 Learning Rate: 0.1 Momentum: 0.9

21. Viewing the results in bar-graphs:

22. Database for Value Prediction: D1 Sunny 80 High Weak No D2 Sunny 75 High Strong No D3 Overcast 70 High Weak Yes D4 Rain 55 High Weak Yes D5 Rain 32 Normal Weak Yes D6 Rain 35 Normal Strong No D7 Overcast 40 Normal Strong Yes D8 Sunny 60 High Weak No D9 Sunny 20 Normal Weak Yes D10 Rain 67 Normal Weak Yes D11 Sunny 62 Normal Strong Yes D12 Overcast 58 High Strong Yes D13 Overcast 74 Normal Weak Yes D14 Rain 61 High Strong No

23. Results of PlayTennis: In-sample: 2 Out-sample: 1 In-sample: 2 Out-sample: 1

24. One partition of the PlayTennis-Prediction:

25. Textual Representation of a single partition:

26. Sequential Patterns Mining and Time Sequence Mining: Sequential patterns are used to find predictable patterns of behavior over a period of time. (A certain behavior at a given time is likely to produce another behavior or a sequence of behaviors within a certain time-span) Time sequences help find all occurrences of similar subsequences in a database of time sequences.

27. Sequences: Combine several objects into a single object that you can run The benefit is that you can combine several steps into one step If you combine several functions into a sequence, you need run only the sequence, which then runs each of the objects within it

28. Applications: The Intelligent Miner offerings are intended for use by Data Analysts and Business Technologists in the following areas: Perform database marketing Streamline business and manufacturing processes Detect potential cases of fraud Helps in customer relationship management