| Abstract |
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Data mining is a field which explores for exciting knowledge or information from existing substantial group of data. In particular, algorithms like Apriori aid a researcher to understand the potential knowledge, deep inside the database. However because of the huge time consumed by Apriori to find the frequent item sets and generate rules, several applications cannot use this algorithm. In this paper, the authors describe a novel method for frequent pattern mining, a variation of Apriori Algorithm, which will reduce the time taken for execution to a larger extent. Experiments were conducted with a number of benchmark and real time data sets and it is found that the new algorithm, proposed has better performance in terms of time taken and complexity |
