NDLI: Offering Pattern Mining Using High Yield Partition Trees

Content Provider	IEEE Xplore Digital Library
Author	Jianying Hu Mojsilovic, A.
Copyright Year	2006
Description	Author affiliation: IBM Thomas J. Watson Res. Center, Yorktown Heights, NY (Jianying Hu; Mojsilovic, A.)
Abstract	Despite the wide use of data mining techniques in client segmentation and market analysis applications, so far there have been no algorithms that allow for the discovery of strategically important combinations of products (or offerings) - the ones that have the highest impact on the performance of the company We present a novel algorithm for analyzing a multi-product environment and identifying strategically important combinations of offerings with respect to a predefined criterion, such as revenue impact, profit impact, inventory turnover etc. In contrast to the traditional association rule and frequent item mining techniques, the goal of the new algorithm is to find segments of data, defined through combinations of products (rules), which satisfy certain conditions as a group. We present a novel algorithm to derive specialized partition threes, called high yield partition trees, which lead to such segments, and investigate different splitting strategies. The algorithm has been tested on real-world data, and achieved very good performance
Sponsorship	The Inst. of Electr. and Electron. Eng., Inc. Circuits and Syst. Soc. (IEEE CASS)
File Size	96648
File Format	PDF
ISBN	142440469X
ISSN	15206149
DOI	10.1109/ICASSP.2006.1661380
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-05-14
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Partitioning algorithms Data mining Algorithm design and analysis Association rules Image segmentation Performance analysis Testing Companies Couplings Pattern recognition
Content Type	Text
Resource Type	Article

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