International Journal of Computer Science & Engineering Technology

ISSN : 2229-3345

Open Access
Open Access

ABSTRACT

Title : Comparative Study of Different Clustering Algorithms for Association Rule Mining
Authors : Ms. Pooja Gupta, Ms. Monika Jena, Ms. Manisha Chowdhary, Ms. Shilpi Singh
Keywords : Apriori algorithm, Clustering, Statistical methods, Hierarchical methods, Density Based method, Grid based method, CHAMELEON, BIRCH, DBSCAN, CLARANS.
Issue Date : May 2013
Abstract :
In data mining, association rule mining is an important research area in today’s scenario. Various association rule mining can find interesting associations and correlation relationship among a large set of data items[1]. To find association rules for single dimensional database Apriori algorithm is appropriate. For large databases lots of candidate sets are generated. Thus Apriori algorithm is not efficient for large databases. We need some extension in the existing Apriori algorithm so that it can also work for large multidimensional database or quantitative database. For this purpose to work with apriori in large multidimensional database, data is divided into multiple data sets called as clusters. In order to divide large data bases into clusters we need various clustering algorithms which can be based on Statistical methods, Hierarchical methods, Density Based method or Grid based method. Once clusters are created by these clustering algorithms, the apriori algorithm can be easily applied on clusters of our interest for mining association rules. Since overall process of finding association rules highly depends on clustering algorithms so we have to use best suited clustering algorithm according to given data base ,thus overall execution time will be reduced. In this paper we have compared various clustering algorithms according to size of data set and type of data set.
Page(s) : 592-595
ISSN : 2229-3345
Source : Vol. 4, Issue.5

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