Knowledgebase approximation using association rule aggregation

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