Paper Title
Implementation and Comparative Study of Improved Apriori Algorithm For Association Pattern Mining

The data mining includes different kinds of data models for analysing the data. The data mining based analysis leads to produce the outcomes according to the employed algorithms over similar kind of data. Thus according to the databases and their mining based outcomes data mining algorithms can be classified in association rule mining algorithm, classification algorithm or clustering methods. In literature a number of different techniques and algorithms are available by which the association rules are mined for different purposes. Among them Apriori algorithm is one of most popular technique of association rule mining. The Apriori algorithm is basically used for transactional pattern analysis using the frequent pattern evaluation of target item sets. Therefore to execute the process, algorithm generates the candidate sets for association pattern analysis. In this presented work first the implementation of Apriori algorithm is performed and then to reduce the time and space complexity a new technique using the Apriori algorithm is performed for finding the association rules. In the proposed rule mining technique the candidate generation is limited by pre-analysis of itemsets during candidate set generation process. Due to this less number of candidates and high quality rules are formed. The implementation of Apriori algorithm is performed using JAVA technology. Additionally the performance in terms of space and time complexity is measured. According to comparative performance study proposed Apriori algorithm provides better and less number of rules in efficient manner. Keywords- Transactional database, Association pattern, Apriori algorithm, implementation, candidate set regulation.