DISTRIBUTED MINING OF ASSOCIATION RULES BASED ON REDUCING THE SUPPORT THRESHOLD

Author:

BOUTSINAS BASILIS1,SIOTOS COSTAS2,GEROLIMATOS ANTONIS2

Affiliation:

1. Dept. of Business Administration, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, 26500 Rio, Patras, Greece

2. UPAIRC, 26500 Rio, Patras, Greece

Abstract

One of the most important data mining problems is learning association rules of the form "90% of the customers that purchase product x also purchase product y". Discovering association rules from huge volumes of data requires substantial processing power. In this paper we present an efficient distributed algorithm for mining association rules that reduces the time complexity in a magnitude that renders as suitable for scaling up to very large data sets. The proposed algorithm is based on partitioning the initial data set into subsets and processing each subset in parallel. The proposed algorithm can maintain the set of association rules that are extracted when applying an association rule mining algorithm to all the data, by reducing the support threshold during processing the subsets. The above are confirmed by empirical tests that we present and which also demonstrate the utility of the method.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

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