Skip to main content
SpringerLink
Log in
Book cover

Inventive Communication and Computational Technologies pp 317–323Cite as

A Survey on K-Means Clustering for Analyzing Variation in Data

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 89))

Abstract

Most of the times data for certain task seems to be varying due constant changes made to method of data collection as well as due to inclusion of new parameters related to the task. This may result in false conclusion derived from data generated and might lead to failure in task or degradation in the standard of activity related to that task which is being monitored from that data. Clustering is basically the grouping of similar kind of data wherein each cluster consist of data with some similarities. Whereas most of the data is unstructured or semi-structured, and that’s where unsupervised K-means Clustering method plays role to convert the data into structured one’s for clustering. This paper consist of K-means clustering method which is being used to keep an eye on such variations which are occurring in data generated for a task when certain changes are incorporated in technique to track this data.

This is a preview of subscription content, 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 EPUB and 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
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Bibliography

  1. Kodinariya TM (2013) Review on determining number of Cluster in k-means. 1(6)

    Google Scholar 

  2. Pattern recognition and machine learning book by Christopher Bishop

    Google Scholar 

  3. Gondaliya B (2014) Review paper on clustering techniques. 2(7). ISSN 2349-4476

    Google Scholar 

  4. Koundinya AK, Srinath NK, Anchalia PP (2013) Mapreduce design of K-means clustering algorithm

    Google Scholar 

  5. Santini M (2016) Machine learning for language technology ML4LT

    Google Scholar 

  6. http://www.datascience.com/

  7. http://www.geeksforgeeks.org/k-means-clustering-introduction/

  8. Theodoridis S Koutroumbas K (2003) Pattern Recognition. Elsevier Science

    Google Scholar 

  9. Kleinberg, J (2003) An impossibility theorem for clustering. In: Advances in neural information processing systems. pp 463–470

    Google Scholar 

  10. Han J, Kamber M (2001) Data mining: concepts and techniques. Morgan Kaufmann

    Google Scholar 

  11. Kaufman L, Rousseuw P (1990) Finding groups in data—an introduction to cluster analysis. In: Wiley series in probability and mathematical statistics

    Google Scholar 

  12. Machine Learning For Dummies Book by John Mueller and Luca Massaron

    Google Scholar 

  13. Ester M, Kriegel H-P, Sander J, Xiaowei X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34):226–231

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. M.Tech Embedded Systems, VIT, Vellore, Tamil Nadu, India

    Pratik Patil

  2. School of Electronics Engg, VIT, Vellore, Tamil Nadu, India

    A. Karthikeyan

Authors
  1. Pratik Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Karthikeyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pratik Patil .

Editor information

Editors and Affiliations

  1. Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Department of Electrical Engineering, Dayeh University, Chang-Hwa, Taiwan

    Joy Chen

  3. University of Coimbra, Rio Tinto, Portugal

    Álvaro Rocha

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Patil, P., Karthikeyan, A. (2020). A Survey on K-Means Clustering for Analyzing Variation in Data. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 89. Springer, Singapore. https://doi.org/10.1007/978-981-15-0146-3_29

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0146-3_29

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0145-6

  • Online ISBN: 978-981-15-0146-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation