Mining evolving customer-product relationships in multi-dimensional space

Xiaolei Li; Jiawei Han; Xiaoxin Yin; Dong Xin

doi:10.1109/ICDE.2005.88

Mining evolving customer-product relationships in multi-dimensional space

Xiaolei Li, Jiawei Han, Xiaoxin Yin, Dong Xin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Previous work on mining transactional database has focused primarily on mining frequent itemsets, association rules, and sequential patterns. However, interesting relationships between customers and items, especially their evolution with time, have not been studied thoroughly. In this paper, we propose a Gaussian transformation-based regression model that captures time-variant relationships between customers and products. Moreover, since it is interesting to discover such relationships in a multi-dimensional space, an efficient method has been developed to compute multi-dimensional aggregates of such curves in a data cube environment. Our experimental results have demonstrated the promise of the approach.

Original language	English (US)
Title of host publication	Proceedings - 21st International Conference on Data Engineering, ICDE 2005
Pages	580-581
Number of pages	2
DOIs	https://doi.org/10.1109/ICDE.2005.88
State	Published - 2005
Event	21st International Conference on Data Engineering, ICDE 2005 - Tokyo, Japan Duration: Apr 5 2005 → Apr 8 2005

Publication series

Name	Proceedings - International Conference on Data Engineering
ISSN (Print)	1084-4627

Other

Other	21st International Conference on Data Engineering, ICDE 2005
Country/Territory	Japan
City	Tokyo
Period	4/5/05 → 4/8/05

ASJC Scopus subject areas

Software
Signal Processing
Information Systems

Online availability

10.1109/ICDE.2005.88

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Li, X, Han, J, Yin, X & Xin, D 2005, Mining evolving customer-product relationships in multi-dimensional space. in Proceedings - 21st International Conference on Data Engineering, ICDE 2005. Proceedings - International Conference on Data Engineering, pp. 580-581, 21st International Conference on Data Engineering, ICDE 2005, Tokyo, Japan, 4/5/05. https://doi.org/10.1109/ICDE.2005.88

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author = "Xiaolei Li and Jiawei Han and Xiaoxin Yin and Dong Xin",

year = "2005",

doi = "10.1109/ICDE.2005.88",

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isbn = "0769522858",

series = "Proceedings - International Conference on Data Engineering",

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N2 - Previous work on mining transactional database has focused primarily on mining frequent itemsets, association rules, and sequential patterns. However, interesting relationships between customers and items, especially their evolution with time, have not been studied thoroughly. In this paper, we propose a Gaussian transformation-based regression model that captures time-variant relationships between customers and products. Moreover, since it is interesting to discover such relationships in a multi-dimensional space, an efficient method has been developed to compute multi-dimensional aggregates of such curves in a data cube environment. Our experimental results have demonstrated the promise of the approach.

AB - Previous work on mining transactional database has focused primarily on mining frequent itemsets, association rules, and sequential patterns. However, interesting relationships between customers and items, especially their evolution with time, have not been studied thoroughly. In this paper, we propose a Gaussian transformation-based regression model that captures time-variant relationships between customers and products. Moreover, since it is interesting to discover such relationships in a multi-dimensional space, an efficient method has been developed to compute multi-dimensional aggregates of such curves in a data cube environment. Our experimental results have demonstrated the promise of the approach.

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M3 - Conference contribution

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SP - 580

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BT - Proceedings - 21st International Conference on Data Engineering, ICDE 2005

T2 - 21st International Conference on Data Engineering, ICDE 2005

Y2 - 5 April 2005 through 8 April 2005

ER -

Mining evolving customer-product relationships in multi-dimensional space

Abstract

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