@article{(Open Science Index):https://publications.waset.org/pdf/9997173,
	  title     = {Analysis of Diverse Clustering Tools in Data Mining},
	  author    = {S. Sarumathi and  N. Shanthi and  M. Sharmila},
	  country	= {},
	  institution	= {},
	  abstract     = {Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {7},
	  number    = {12},
	  year      = {2013},
	  pages     = {1633 - 1637},
	  ee        = {https://publications.waset.org/pdf/9997173},
	  url   	= {https://publications.waset.org/vol/84},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 84, 2013},
	}