A Method For Generating Aggregated
Associations Between Discrete Data Features
Author(s)
E. Titova, D. Sitnikov, O. Ryabov, B. D’Cruz
Abstract
The existing algorithms for discovering association rules in databases have been
developed mainly for binary features (i.e. a feature is either present in a
transaction or not). Nevertheless, in real databases it often happens that a feature
can take on values from an arbitrary finite set. Such features are considered to be
categorical values. In many cases continuous data is binned to obtain a set of
discrete values. We suggest an algorithm for discovering generalised association
rules in databases containing categorical data. This algorithm is based on
building a tree of covers which allows us to generate associations with necessary
support, and also to take into account updated records. To discover generalised
associations we use a method of merging different branches of the cover tree for
given values of a feature. The resulting rules look as follows: if the feature X
takes on values from the set {a1,a2,...ai}, then feature Y takes on values from the
set {b1,...,bk}. This is beneficial for small discrete feature domains.
Keywords: data mining, generalized association rules, finite predicate.
1 Introduction
Discovering association rules is a typical task for Data Mining. The process of
generating associations consists of obtaining rules in the implicative form.
Originally this task appeared in the marketing area (so called \“market basket
Keywords
data mining, generalized association rules, finite predicate.
Related Book
Other papers in this volume
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