|International Journal of Computer Applications
|Foundation of Computer Science (FCS), NY, USA
|Volume 112 - Number 4
|Year of Publication: 2015
|Authors: Minal G. Ingle, N. Y. Suryavanshi
Minal G. Ingle, N. Y. Suryavanshi . Association Rule Mining using Improved Apriori Algorithm. International Journal of Computer Applications. 112, 4 ( February 2015), 37-42. DOI=10.5120/19658-1297
The past few years have seen a marvellous curiosity in area of data mining. Data mining is usually thought of as the process of finding hidden, non trivial and formerly unknown information in large collection of data. Association rule mining is an significant component of data mining. Association rule are an important class of methods of finding regularities or patterns in data. Association rule mining has been used in several application domains. The ideal application of Association Rule Mining is market basket analysis. Apriori algorithm generates interesting frequent or infrequent candidate item sets with respect to support count. Apriori algorithm can require to produce vast number of candidate sets. To generate the candidate sets, it needs several scans over the database. Apriori acquires more memory space for candidate generation process. While it takes multiple scans, it must require a lot of I/O load. The approach to overcome the difficulties is to get better Apriori algorithm by making some improvements in it. Also will develop pruning strategy as it will decrease the scans required to generate candidate item sets and accordingly find a valence or weightage to strong association rule. So that, memory and time needed to generate candidate item sets in Apriori will reduce. And the Apriori algorithm will get more effective and efficient.