Research on Mining Sequential Positive and Negative Association Rules

Abstract

Mining sequential positive and negative association rules is to mine the inner association or the causal relationship among data in sequential database, which will find some rules that have practical significance for the industry decision-making analysis among the sequence. This paper proposes the relational notions of sequential positive and negative association rule. Based on the new questions when mining the positive and negative rules in the sequential database, the paper discusses the solutions and proposes an algorithm called SPNARM to mine sequential positive and negative association rules (SPNAR). Example analysis results show that SPNARM algorithm is more efficient for mining SPNARs.