404 Test 2 Prep Notes

404 Test 2 Prep Notes - Interesting Measures Simplicity for...

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Interesting Measures Simplicity for human comprehension Rule length – may be calculated by # conjuncts in rule (Note: each conjunct adds further restriction to the rule.) Certainty: Each discovered pattern should have a measure of certainty associated with it that assesses the validity or “trustworthiness” of the pattern. A certainty measure for association rules of the form “A => B”, where A and B are sets of items, is confidence. Given a set of task-relevant data tuples (or transactions in a transaction database) the confidence of “A => B” is defined as A containing tuples B and A both containing tuples A B P B A confidence _ _ _ # _ _ _ _ _ # ) | ( ) ( = = = A confidence value of 100% or 1, indicates that the rule is always correct on the data analyzed. Such rules are called exact. For classification rules the equation can easily be adapted to act as a measure for certainty referred to as reliability or accuracy. Classification rules propose a model for distinguishing objects, or tuples, of a target class from objects of contrasting classes. A low reliability value indicates that the rule in question incorrectly classifies a large number of contrasting class objects as target class objects. Rule reliability is also knowing as rule strength, rule quality, certainty factor, and discriminating weight. Utility : The potential usefulness of a patter is a factory defining its interestingness. It can be estimated by a utility function, such as support. The support of an association pattern refers to the percentage of task- relevant data tuples or transitions for which the pattern is true. For association rules of the form “A => B” where A and B are sets of items, it is defined as tuples total B and A both containing tuples A B P B A _ _# _ _ _ _ _ # ) ( ) ( support = = =

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404 Test 2 Prep Notes - Interesting Measures Simplicity for...

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