Mining allocating patterns in one-sum weighted items

Wang, Yanbo J., Zheng, Xinwei, Coenen, Frans and Li, Cindy Y. 2008, Mining allocating patterns in one-sum weighted items, in ICDMW 2008 : Proceedings of the International Conference on Data Mining Workshops, IEEE, Piscataway, N.J., pp. 592-598, doi: 10.1109/ICDMW.2008.112.

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Title Mining allocating patterns in one-sum weighted items
Author(s) Wang, Yanbo J.
Zheng, XinweiORCID iD for Zheng, Xinwei orcid.org/0000-0001-5970-4609
Coenen, Frans
Li, Cindy Y.
Conference name IEEE International Conference on Data Mining Workshops (2008: Pisa, Italy)
Conference location Pisa, Italy
Conference dates 15-19 Dec. 2008
Title of proceedings ICDMW 2008 : Proceedings of the International Conference on Data Mining Workshops
Editor(s) Bonchi, Francesco
Berendt, Bettina
Giannotti, Fosca
Gunopulos, Dimitrios
Turini, Franco
Zaniolo, Carlo
Ramakrishnan, Naren
Wu, Xindong
Publication date 2008
Start page 592
End page 598
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Summary 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.
ISBN 9780769535036
Language eng
DOI 10.1109/ICDMW.2008.112
Field of Research 150299 Banking, Finance and Investment not elsewhere classified
Socio Economic Objective 970115 Expanding Knowledge in Commerce, Management, Tourism and Services
HERDC Research category EN.1 Other conference paper
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077813

Document type: Conference Paper
Collection: Department of Finance
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