An Efficient Algorithm for Mining Frequent K-Item Sets for Association Rule Mining in Large Databases

Abstract

Data mining is the process of extracting interesting and previously unknown patterns and correlation form huge data stored in data bases. Association rule mining- a descriptive mining technique of data mining is the process of discovering items or literals which tend to occur together in transactions. The problem of the data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items. Most of the previous research based on Apriori, which suffers with generation of huge number of candidate item sets and performs repeated passes for finding frequent item sets. To address this problem, in the proposed algorithm for finding frequent K-item sets in which the database is not used at all for counting the support of candidate item sets after the first pass. This makes the size of the encoding much smaller than the database, thus saving much reading effort. The Experimental Results are included.