An Improved Algorithm of graph and Clustering Based Association Rule Mining (GCBARM) in discovering of frequent Itemsets

Abstract

The key process of Association Rule mining algorithms is discovering frequent itemsets. The new proposed numerous algorithms are an important research topic in the field of data mining. This paper proposed two concepts. First, in huge amount of datasets, basic need of valuable analysis is to finding the relational and geometric characteristics of the underlying entities are represent their relationships with vertices and edges, this method provide to represent such datasets. Second is the proposed algorithm, which based on graph and clustering based mining association rules. This proposed algorithm is named Graph and Clustering Based Association Rule Mining (GCBARM).Scanning is the important task of frequent itemsets, the GCBARM algorithm scans the database of transaction only once to generate a cluster table and then clusters the transactions into cluster according to their length. The GCBARM algorithm is used to find frequent itemsets and will be extracted directly by scanning the cluster table. This method reduces memory requirement and time to retrieve the datasets and hence it is scalable for any large size of the database.

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