TY - GEN

T1 - On the mining of substitution rules for statistically dependent items

AU - Teng, Wei-Guang

AU - Hsieh, Ming Jyh

AU - Chen, Ming Syan

PY - 2002/12/1

Y1 - 2002/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:78149301725

SN - 0769517544

SN - 9780769517544

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 442

EP - 449

BT - Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002

T2 - 2nd IEEE International Conference on Data Mining, ICDM '02

Y2 - 9 December 2002 through 12 December 2002

ER -