DMW1

Association rule discovery

Clustering

Classification

Data collection

Pattern extraction and discovery

Data preprocessing

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Description

Prediction

Prescription

Description

Prediction

Prescription

Prediction

Prescription

Description

with large-sized datasets

when irrelevant attributes have been removed from the data

when a generalized model of the data is desireable

when an explanation of what has been found is of primary importance

Outliers should be identified and removed from a dataset

Outliers should be part of the training dataset but should not be present in the test

data

Outliers should be part of the test dataset but should not be present in the training

data

The nature of the problem determines how outliers are used

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70

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27

allow the same variable to be an input attribute in one rule and an output attribute in

another rule

allow more than one input attribute in a single rule

require input attributes to take on numeric values

require each rule to have exactly one categorical output attribute