A Method For Association Rule Quality
Evaluation Based On Information Theory
Author(s)
D. Sitnikov, E. Titova & O. Ryabov
Abstract
The concept of patterns representing functional, logical and other dependencies
in data lies in the basis of the Data Mining technology. One of the wide spread
forms for representing discovered knowledge patterns is association rules. A
method for evaluating an association rule from the viewpoint of information
theory has been suggested, which allows us to calculate a generalized
characteristic of associations (based on mutual information) with the help of the
well known association rule parameters: Support, Confidence and Improvement.
Using such a characteristic of associations complements the traditional
association parameters and allows setting a linear order on the set of
associations, which is useful for evaluating and filtering obtained dependencies.
Besides we have carried out analysis of the dependence of the association rule
self-descriptiveness on the standard parameters.
1 Introduction
A general definition of association rules has been suggested in [1]: Let
m 2 1 I ,..., I , I L =be a set of object features. Let Т be a set of records. Each record
t is represented by a binary vector 1 ] k [ t =if t contains the feature k I and
0 ] k [ t =if t does not contain the feature k I ) m , 1 k (=. Let X be a subset
including some features from L, i.e. L X ⊆. We say that the record t satisfies X
if , X I k ⊆∀1 ] k [ t =. An association rule is an expression in the form Y X →,
where L X ⊆, L Y ⊆, at that ∅=∩Y X .
Keywords
Related Book
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