Mining Allocating Patterns in One-Sum Weighted Items

Abstract

An association rule (AR) is a common knowledge model in data mining that describes an implicative co-occurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent rArr consequent" rule. A variant of the AR is the weighted association rule (WAR). With regard to a marketing context, this paper introduces a new knowledge model in data mining - allocating pattern (ALP). An ALP is a special form of WAR, where each rule item is associated with a weighting score between 0 and 1, and the sum of all rule item scores is 1. It can not only indicate the implicative co-occurring relationship between two (disjoint) sets of items in a weighted setting, but also inform the "allocating" relationship among rule items. ALPs can be demonstrated to be applicable in marketing and possibly a surprising variety of other areas. We further propose an apriori based algorithm to extract hidden and interesting ALPs from a "one-sum" weighted transaction database. The experimental results show the effectiveness of the proposed algorithm.