An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining negative generalized knowledge from relational databases
Knowledge-Based Systems
A generic approach for mining indirect association rules in data streams
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Inductive learning of disjointness axioms
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
Mining high coherent association rules with consideration of support measure
Expert Systems with Applications: An International Journal
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In this paper, a new mining capability, called mining ofsubstitution rules, is explored. A substitution refers to thechoice made by a customer to replace the purchase of someitems with that of others. The process of mining substitutionrules can be decomposed into two procedures. The first procedureis to identify concrete itemsets among a large numberof frequent itemsets, where a concrete itemset is a frequentitemset whose items are statistically dependent. Thesecond procedure is then on the substitution rule generation.Two concrete itemsets X and Y form a substitutionrule, denoted by X \triangleright 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 \to \overline Y exists. In this paper,we derive theoretical properties for the model of substitutionrule mining. Then, in light of these properties, algorithmSRM (standing for substitution rule mining) is designedand implemented to discover the substitution rulesefficiently while attaining good statistical significance. Empiricalstudies are performed to evaluate the performance ofalgorithm SRM proposed. It is shown that algorithm SRMproduces substitution rules of very high quality.