Affiliation:
1. Delhi Skill and Entrepreneurship University, New Delhi, India
Abstract
Clustering is the grouping together of similar data items into clusters. Clustering analysis is one of the main analytical methods in data mining; the method of clustering algorithm will influence the clustering results directly. This paper discusses the various types of algorithms like Hierarchical Clustering Algorithms Partitioning Method Nearest Neighbor algorithm K-Mean (A centroid based Technique) Density-Based clustering etc. This paper also deals with the problems of clustering algorithm such as time complexity and accuracy to provide the better results based on various environments.
Reference15 articles.
1. L. Parsons, E. Haque, and H. Liu, "Subspace clustering forhigh dimensional data: a review," ACM SIGKDD Explorations Newsletter, vol. 6, pp. 90-105, 2004.
2. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, "A framework for projected clustering of high dimensional data streams," in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, 2004, p.863.
3. R. Agrawal, J. E. Gehrke, D. Gunopulos, and P. Raghavan, "Automatic subspace clustering of high dimensional data fordata mining applications," Google Patents, 1999.
4. C. Domeniconi, D. Papadopoulos, D. Gunopulos, and S. Ma, "Subspace clustering of high dimensional data," 2004.
5. X. Z. Fern and C. E. Brodley, "Random projection for high dimensional data clustering: A cluster ensemble approach,"2003, p. 186.