Knowledgebase Approximation Using Association Rules Aggregation

Abstract This paper introduces knowledgebase approximation and fusion using association rules aggregation as a means to facilitate accelerated insight induction from high-dimensional and disparate knowledgebases. There are two typical observations that make approximating knowledgebases of interest: (1) it is quite often that insights can be derived based from a partial set of the samples, and not necessarily from all of them; and (2) generally speaking, it is rare that the knowledge of interest is contained in one knowledgebase, but rather distributed among a disparate set of unidentical knowledgebases. As a matter of fact, the insights derivable from knowledgebases tend to be uncertain, even if they were to be derived from a wholistic analysis of the knowledgebase. Thus, optimal knowledgebase approximation may yield the computational efficiency benefit without necessarily compromising insight accuracy. This paper presents a novel method to approximate a set of knowledgebases based on association rule aggregation using the disjunctive pooling rule. We show that this method can reduce insight discovery time while maintaining approximation accuracy within a desirable level.