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 Customer # Date(yymmdd) Transaction # ItemID

Name Mapping:

Results of mining for associations:

Results on the automobile Database:

Another view:

Database used for Clustering: Gender Age Siblings Income Type Product female red 2 female green 3 male red 4 female green 5 male blue 6 female blue 7 female green8 female pink 1 female red 2 female pink3 female green 4 male blue 5 male blue 6 female pink 7 female green 8 male blue 1 male blue 2 female green 3

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

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 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 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

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