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Mining allocating patterns in one-sum weighted items

conference contribution
posted on 2008-01-01, 00:00 authored by Y J Wang, Xinwei ZhengXinwei Zheng, F Coenen, C Y Li
An Association Rule (AR) is a common knowledge model in data mining that describes an implicative cooccurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent⇒ 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. © 2008 IEEE.

History

Event

IEEE International Conference on Data Mining Workshops (2008: Pisa, Italy)

Pagination

592 - 598

Publisher

IEEE

Location

Pisa, Italy

Place of publication

Piscataway, N.J.

Start date

2008-12-15

End date

2008-12-19

ISBN-13

9780769535036

Language

eng

Publication classification

EN.1 Other conference paper

Copyright notice

2008, IEEE

Editor/Contributor(s)

F Bonchi, B Berendt, F Giannotti, D Gunopulos, F Turini, C Zaniolo, N Ramakrishnan, X Wu

Title of proceedings

ICDMW 2008 : Proceedings of the International Conference on Data Mining Workshops

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