Clustering is the popular unsupervised learning technique of data mining which divide the data into groups having similar objects and used in various application areas. k-Means is the most popular clustering algorithm among all partition based clustering algorithm to partition a dataset into meaningful patterns. k-Means suffers some shortcomings. This paper addresses two shortcomings of k-Means; pass number of centroids in apriori and does not handle noise. This paper also presents an overview of cluster analysis, clustering algorithms, preprocessing and normalization techniques in modified k-Means to improve the effectiveness and efficiency of the modified k-Means clustering algorithm.


Algorithm Clustering k-Means Preprocessing Normalization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vaishali R. Patel
    • 1
  • Rupa G. Mehta
    • 2
  1. 1.Department of Computer Science and EngineeringSVMITBharuchIndia
  2. 2.Department of Computer EngineeringSVNITSuratIndia

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