Compression Record Based Efficient k - Medoid Algorithm to Increase Scalability and Efficiency
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 8
Abstract
Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attri butes. K - medoid clustering algorithms are widely used for many practical applications. Original K - medoid algorithm select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it generates unstable and empty clusters which are meaningless. The original k - means algorithm is computationally expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations. Improved k - Medoid clustering algorith m has the accuracy higher than the original .
Authors and Affiliations
Archana Kumari , Hritu Bhagat
Keywords
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