Triangle Counting Approach for graph-based Association Rules Mining

Abstract

Graph-based Association Rules Mining (ARM) is a research area that represents a transactional database into a graph structure to optimize the search for frequent item sets. Sub-graph search is the process of pruning the search by looking for the best representation of connected nodes in a graph to represent the fully connected graphs. Triangle Counting Approach is one of the sub-graph search approaches to find the most represented graph. This study aims to employ the Triangle Counting Approach for graph-based association rules mining. A triangle counting method for graph-based ARM is proposed to prune the graph in the search for frequent item sets. The triangle counting is integrated with one of the graph-based ARM methods. It consists of four important phases; data representation, triangle construction, bit vector representation, and triangle integration with the graph-based ARM method. The performance of the proposed method is compared with the original graph-based ARM. Experimental results show that the proposed method reduces the execution time of rules generation and produces less number of rules with higher confidence.