期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2014
卷号：3
期号：8
页码：2685-2689
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Huge amount of heterogeneous information is available on the web. Clustering is one of the techniques to deal with enormous amount of information. Clustering partitions a data set into groups where data objects in each group should exhibit large degree of similarity. Data objects with high similarity measure should be placed in a cluster (intra cluster). Similarity between the data objects of different clusters should be less (inter cluster). The frequently used partitioning-based clustering algorithm is K-means algorithm. K-means algorithm is simple, straightforward, easy to implement and works efficiently in many applications. K means algorithm has the limitation of generating local optimal solution. Harmony Search Method (HSM) is a new meta- heuristic optimization method which imitates the music improvisation process. HSM has been a successful technique in a wide variety of optimization problem. Better results can be obtained by hybridizing K-means with HSM. Initially, Term Frequency and Inverse Document Frequency(TF-IDF) of a feature can be calculated and the documents are clustered. Later the concept called coverage factor was used to cluster the documents. In the proposed work an effort has been made to apply the concept factorization method for document clustering problem, to find optimal clusters in sufficient amount of time. A comparison has been made among all these methodologies to show that concept factorization produces better results.
关键词：Optimization; Term Frequency-Inverse ; Document Frequency; Coverage Factor; Concept Factorization
