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An Enhanced K-Means Clustering Algorithm to Remove Empty Clusters
Anshul Yadav,  Sakshi Dhingra
Today in the modern era, everyone has to retrieve a large amount of data from a vast collection of data. This process of retrieving useful data in the understandable form is data mining. Clustering is an important data analytic technique which has a significant role in data mining application. Clustering is the method of arranging a set of similar objects into a group. Partition based clustering is an important clustering technique. This technique is centroid based technique in which data points splits into k partition and each partition represents a cluster. A widely used partition based clustering algorithm is k- means clustering algorithm. But this algorithm also has some limitations. These limitations can be reduced by some improvements in existing algorithm. One of the limitations and its development discussed in this paper.
Keywords- Data Mining, Clustering, K-Means.
Unique Identification Number - IJEDR1604137Page Number(s) - 901-907Pubished in - Volume 4 | Issue 4 | December 2016DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Anshul Yadav,  Sakshi Dhingra,   "An Enhanced K-Means Clustering Algorithm to Remove Empty Clusters"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 4, pp.901-907, December 2016, Available at :http://www.ijedr.org/papers/IJEDR1604137.pdf