An Ameliorated Partitioning Clustering Algorithm

Original K-medoid algorithm use to take initial medoids arbitrarily that bears on the resulting clusters and it leads to unstable and empty clusters which are no meaningful and also amount of iterations can be rather high so K-Medoid is not a substitute for big databases because of its computational...

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Bibliographic Details
Published in:	2014 International Conference on Computational Intelligence and Communication Networks pp. 520 - 524
Main Authors:	Chouhan, Raghavi, Chauhan, Abhishek
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.11.2014
Subjects:	Accuracy Algorithm design and analysis centroids Classification algorithms clustering Clustering algorithms Data mining K medoid medoids Partitioning algorithms Prediction algorithms
Online Access:	Get full text
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Summary:	Original K-medoid algorithm use to take initial medoids arbitrarily that bears on the resulting clusters and it leads to unstable and empty clusters which are no meaningful and also amount of iterations can be rather high so K-Medoid is not a substitute for big databases because of its computational complexity. Also the original k-means algorithm is computationally. Though existing algorithms usually leads to better outcome, they do not scale well and are not time efficient. Ameliorated k-Medoid clustering partitioning algorithm is an improved K-Medoid algorithm will have the accuracy more than the original one. The new idea for K-medoid algorithm overcomes the deficiency of existing medoid. It initially computes the initial centroids k as per the necessity of user and then provides improved and efficient cluster with no sacrifice on accuracy. It generates steady clusters in order to get better accuracy. It also minimizes the mean square error and improves the quality of clustering, reduces the number of iterations and works on reducing time complexity.
DOI:	10.1109/CICN.2014.119