Cluster based bit vector mining algorithm for finding frequent itemsets in temporal databases

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. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.