Mining Positive and Negative Association Rules in Data Streams with a Sliding Window

Abstract

Association rule mining is one of the most important data mining techniques. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. All of the literature on negative association mining, to our best knowledge, is confined to the traditional, relatively static database environment, no research work has been conducted on mining negative associations over data streams. In this paper, we propose an algorithm for mining negative associations over data streams. Experiments on the synthetic data stream are performed to show the effectiveness and efficiency of the proposed approach.