Abstract
Abstract In this paper, we introduce an efficient algorithm using a new technique to find frequent itemsets from a huge set of itemsets called Cluster based Bit Vectors for Association Rule Mining (CBVAR). In this work, all the items in a transaction are converted into bits (0 or 1). A cluster is created by scanning the database only once. Then frequent 1-itemsets are extracted directly from the cluster table. Moreover, frequent k-itemsets, where k ≥ 2 are obtained by using Logical AND between the items in a cluster table. This approach reduces main memory requirement since it considers only a small cluster at a time and as scalable for any large size of database. The overall performance of this method is significantly better than that of the previously developed algorithms for effective decision making.
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