Frequent Pattern Mining Using Record Filter Approach

In today’s emerging world, the role of data mining is increasing day by day with the new aspect of business. Data mining has been proved as a very basic tool in knowledge discovery and decision making process. Data mining technologies are very frequently used in a variety of applications. Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters. Frequent patterns are the itemsets that are frequently visited in database transactions at least for the user defined number of times which is known as support threshold. Presently a number of algorithms have been proposed in literature to enhance the performance of Apriori Algorithm, for the purpose of determining the frequent pattern. The main issue for any algorithm is to reduce the processing time. Present paper proposes a new record filtering based approach which takes very less time for performing computations during mining process. Experiments have been performed on synthetic datasets and the results have been presented. The results show that proposed approach performs well in terms of execution time and ultimately enhances efficiency as compared to traditional Apriori approach.