Skip to main content
SpringerLink
Log in

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

  • Conference paper
ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 249))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Haung, Z.: Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Canberra, ACT 2601, Australia (1998)

    Google Scholar 

  2. He, Z., Deng, S., Xu, X.: Improving K-Modes Algorithm Considering Frequencies of Attribute Values in Mode. Harbin Institute of Technology, China (2005)

    Google Scholar 

  3. Haung, Z.: A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining

    Google Scholar 

  4. Sayal, R., Vijay Kumar, V.: A Novel Similarity Measure for Clustering Categorical Data Sets. International Journal of Computer Applications (2011)

    Google Scholar 

  5. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques (2011)

    Google Scholar 

  6. Mastrogiannis, N., Giannikos, I., Boutsinas, B., Antzoulatos, G.: CL.E.KMODES: A modified k-modes clustering algorithm. University of Patras, Greece (2009)

    Google Scholar 

  7. Khan, S.S., Kant, S.: Computation of Initial Modes for K-modes Clustering Algorithm using Evidence Accumulation (2007)

    Google Scholar 

  8. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson education (2006)

    Google Scholar 

  9. He, Z.: Approximation Algorithms for K-Modes Clustering. Harbin Institute of Technology, China (2006)

    Google Scholar 

  10. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Elsevier (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of CSE, GMR Institute of Technology, Rajam, Srikakulam(Dist.), A.P., India

    R. Madhuri & M. Ramakrishna Murty

  2. Dept. of CSE, JNTUK, Kakinada, A.P., India

    J. V. R. Murthy

  3. Dept. of CS&SE, Andhra University, Visakhapatnam, A.P., India

    P. V. G. D. Prasad Reddy

  4. Dept. of CSE, ANITS, Visakhapatnam, A.P., India

    Suresh C. Satapathy

Authors
  1. R. Madhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Ramakrishna Murty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. V. R. Murthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. V. G. D. Prasad Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Suresh C. Satapathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Madhuri .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. College of Engineering(A), Andhra University, Vishakapatnam, India

    P. S. Avadhani

  3. University of Hyderabad, Hyderabad, India

    Siba K. Udgata

  4. CSIR-National Institute of Oceanography, Visakhapatnam, India

    Sadasivuni Lakshminarayana

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Madhuri, R., Murty, M.R., Murthy, J.V.R., Reddy, P.V.G.D.P., Satapathy, S.C. (2014). Cluster Analysis on Different Data Sets Using K-Modes and K-Prototype Algorithms. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II. Advances in Intelligent Systems and Computing, vol 249. Springer, Cham. https://doi.org/10.1007/978-3-319-03095-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03095-1_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03094-4

  • Online ISBN: 978-3-319-03095-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation