Parallel Association Rule Mining for Medical Applications

Abstract

For real-time applications that consist of massive number of rules, partitioning of the rules to support parallel processing is important. This paper proposes a suite of algorithms called GAPCM for parallel processing of massive number of rules. By considering even distribution, minimal waiting time and minimal inter-processor communication, we propose three algorithms for subnet allocation, and apply these algorithms to association rule mining.