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An overview of The IBM Intelligent Miner for Data By: Neeraja Rudrabhatla 11/04/1999 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 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 Main Window of the Data Miner: Database used for mining association rules: Store ID 001 001 001 001 001 001 001 001 001 001 001 001 001 001 001 001 001 001 001 001 001 Customer # Date(yymmdd) Transaction # 0000007 950109 00982 0000007 950109 00982 0000007 950109 00982 0000007 950109 00982 0000003 950109 00983 0000003 950109 00983 0000003 950109 00983 0000003 950109 00983 0000005 950109 00984 0000005 950109 00984 0000005 950109 00984 0000005 950109 00984 0000008 950109 00985 0000008 950109 00985 0000008 950109 00985 0000008 950109 00985 0000006 950109 00986 0000006 950109 00986 0000006 950109 00986 0000006 950109 00986 0000002 950109 00987 ItemID 122 125 133 150 153 154 162 166 147 174 191 198 147 174 182 184 174 186 187 188 109 Name Mapping: 101 102 103 104 105 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 Cream A-Beer B-Beer C-Beer Stout Export Cider Milk Antifreeze Port wine White German Red German wi White French Red French wi White Italian Red Italian w Sherry Champagne Sekt Asti Spumante Crackers Salty biscuit Crisps Cheddar Chees Gouda Cheese Cottage chees Irish Butter Results of mining for associations: Results on the automobile Database: Another view: Database used for Clustering: Gender female female male female male female female female female female female male male female female male male female Age 18.02 13.03 11.0 47.5 11.07 24.0 62.1 04.08 40.1 04.08 45.8 21.07 07.02 42.5 36.9 10.03 02.03 20.0 Siblings Income 1 97 6 490 3 647 2 3192 5 736 3 22358 0 3936 1 516 0 9478 0 193 5 16984 0 10428 0 960 0 10835 2 37083 3 877 0 10 0 15432 Type red green red green blue blue green pink red pink green blue blue pink green blue blue green Product 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 Clustering - Demographic: Max #clusters: 9 Accuracy: 5% Details of Cluster 7: Detailed pie-chart for attribute Type: Detailed bar-graph of attribute Age: Output obtained with Clustering using Neural Networks: Details of Cluster 6: Database used for Classification: Day D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 Outlook Temperature Humidity Sunny Hot High Sunny Hot High Overcast Hot High Rain Mild High Rain Cool Normal Rain Cool Normal Overcast Cool Normal Sunny Mild High Sunny Cool Normal Rain Mild Normal Sunny Mild Normal Overcast Mild High Overcast Hot Normal Rain Mild High Wind PlayTennis Weak No Strong No Weak Yes Weak Yes Weak Yes Strong No Strong Yes Weak No Weak Yes Weak Yes Strong Yes Strong Yes Weak Yes Strong No Classification using Decision Tree: A view of a leaf node of the decision tree: Classification using neural network: In-sample: 4 Out-Sample: 1 Accuracy: 80 Error: 10 Learning Rate: 0.1 Momentum: 0.9 Viewing the results in bar-graphs: Database for Value Prediction: D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 Sunny Sunny Overcast Rain Rain Rain Overcast Sunny Sunny Rain Sunny Overcast Overcast Rain 80 75 70 55 32 35 40 60 20 67 62 58 74 61 High High High High Normal Normal Normal High Normal Normal Normal High Normal High Weak Strong Weak Weak Weak Strong Strong Weak Weak Weak Strong Strong Weak Strong No No Yes Yes Yes No Yes No Yes Yes Yes Yes Yes No Results of PlayTennis: In-sample: 2 Out-sample: 1 One partition of the PlayTennis-Prediction: Textual Representation of a single partition: 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. 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 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
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