An overview of The IBM Intelligent Miner for Data

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An overview of The IBM Intelligent Miner for Data

• By Neeraja Rudrabhatla
• 11/04/1999

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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

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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

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Main Window of the Data Miner
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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
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Name Mapping
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Results of mining for associations
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Results on the automobile Database
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Another view
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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
  green 8
• female 04.08 1 516 pink
  1
• female 40.1 0 9478 red
  2
• female 04.08 0 193
  pink 3
• 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

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Clustering - Demographic
Max clusters 9 Accuracy 5
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Details of Cluster 7
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Detailed pie-chart for attribute Type
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Detailed bar-graph of attribute Age
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Output obtained with Clustering using Neural
Networks
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Details of Cluster 6
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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

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Classification using Decision Tree
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A view of a leaf node of the decision tree
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Classification using neural network
In-sample 4 Out-Sample 1 Accuracy 80 Error
10 Learning Rate 0.1 Momentum 0.9
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Viewing the results in bar-graphs
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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
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Results of PlayTennis
In-sample 2 Out-sample 1
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One partition of the PlayTennis-Prediction
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Textual Representation of a single partition
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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.

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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

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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