Review based on data clustering algorithms

• Published in: 2013 IEEE Conference on Information and Communication Technologies pp. 298 - 303
• Main Authors: Nagpal, Arpita, Jatain, Aman, Gaur, Deepti
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2013
• Subjects: Algorithm design and analysis; Clustering algorithms; Clustering methods; Communications technology; Conferences; Couplings; Data Clustering; hierarchical clustering; Partitioning algorithms; Special; Statistical analysis; temporal
• ISBN: 9781467357593; 1467357596
• DOI: 10.1109/CICT.2013.6558109

A review based on different types of clustering algorithms with their corresponding data sets has been proposed. In this paper, we have given a complete comparative statistical analysis of various clustering algorithms. Clustering algorithms usually employ distance metric or similarity metric to cluster the data set into different partitions. Well known clustering algorithms have been widely used in various disciplines. Type of clustering algorithm used depends upon the application and data set used in that field. Numerical data set is comparatively easy to implement as data are invariably real number and can be used for statistical applications. Others type of data set such as categorical, time series, boolean, and spatial, temporal have limited applications. By viewing the statistical analysis, it is observed that there is no optimal solution for handling problems with large data sets of mixed and categorical attributes. Some of the algorithms can be applied but their performance degrades as the size of data keeps on increasing.