On the mining of substitution rules for statistically dependent items

Wei-Guang Teng, Ming Jyh Hsieh, Ming Syan Chen

研究成果: Conference contribution

42 引文 斯高帕斯(Scopus)

摘要

In this paper, a new mining capability, called mining of substitution rules, is explored. A substitution refers to the choice made by a customer to replace the purchase of some items with that of others. The process of mining substitution rules can be decomposed into two procedures. The first procedure is to identify concrete itemsets among a large number of frequent itemsets, where a concrete itemset is a frequent itemset whose items are statistically dependent. The second procedure is then on the substitution rule generation. Two concrete itemsets X and Y form a substitution rule, denoted by X▷Y to mean that X is a substitute for Y, if and only if (1) X and Y are negatively correlated and (2) the negative association rule X→ Ȳ exists. In this paper, we derive theoretical properties for the model of substitution rule mining. Then, in light of these properties, algorithm SRM (standing for substitution rule mining) is designed and implemented to discover the substitution rules efficiently while attaining good statistical significance. Empirical studies are performed to evaluate the performance of algorithm SRM proposed. It is shown that algorithm SRM produces substitution rules of very high quality.

原文English
主出版物標題Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
頁面442-449
頁數8
出版狀態Published - 2002 十二月 1
事件2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
持續時間: 2002 十二月 92002 十二月 12

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(列印)1550-4786

Other

Other2nd IEEE International Conference on Data Mining, ICDM '02
國家Japan
城市Maebashi
期間02-12-0902-12-12

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • 引用此

    Teng, W-G., Hsieh, M. J., & Chen, M. S. (2002). On the mining of substitution rules for statistically dependent items. 於 Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002 (頁 442-449). (Proceedings - IEEE International Conference on Data Mining, ICDM).