Cluster Analysis on Different Data Sets Using K-Modes and K-Prototype Algorithms

  • R. Madhuri
  • M. Ramakrishna Murty
  • J. V. R. Murthy
  • P. V. G. D. Prasad Reddy
  • Suresh C. Satapathy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


The k-means algorithm is well-known for its efficiency in clustering large data sets and it is restricted to the numerical data types. But the real world is a mixture of various data typed objects. In this paper we implemented algorithms which extend the k-means algorithm to categorical domains by using Modified k-modes algorithm and domains with mixed categorical and numerical values by using k-prototypes algorithm. The Modified k-modes algorithm will replace the means with the modes of the clusters by following three measures like “using a simple matching dissimilarity measure for categorical data”, “replacing means of clusters by modes” and “using a frequency-based method to find the modes of a problem used by the k-means algorithm”. The other algorithm used in this paper is the k-prototypes algorithm which is implemented by integrating the Incremental k-means and the Modified k-modes partition clustering algorithms. All these algorithms reduce the cost function value.


Cluster K-means K-modes K-prototypes mixed data 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • R. Madhuri
    • 1
  • M. Ramakrishna Murty
    • 1
  • J. V. R. Murthy
    • 2
  • P. V. G. D. Prasad Reddy
    • 3
  • Suresh C. Satapathy
    • 4
  1. 1.Dept. of CSEGMR Institute of TechnologyRajamIndia
  2. 2.Dept. of CSEJNTUKKakinadaIndia
  3. 3.Dept. of CS&SEAndhra UniversityVisakhapatnamIndia
  4. 4.Dept. of CSEANITSVisakhapatnamIndia

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