Skip to main content
SpringerLink
Log in

Clustering Validity

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 180 Accesses

Synonyms

Cluster stability; Cluster validation; Quality assessment; Stability-based validation of clustering

Definition

A problem one faces in clustering is to decide the optimal partitioning of the data into clusters. In this context visualization of the data set is a crucial verification of the clustering results. In the case of large multidimensional data sets (e.g., more than three dimensions) effective visualization of the data set is cumbersome. Moreover the perception of clusters using available visualization tools is a difficult task for humans that are not accustomed to higher dimensional spaces. The procedure of evaluating the results of a clustering algorithm is known under the term cluster validity. Cluster validity consists of a set of techniques for finding a set of clusters that best fits natural partitions (of given datasets) without any a priori class information. The outcome of the clustering process is validated by a cluster validity index.

Historical Background

Clust...

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

Access this chapter

Log in via an institution

Institutional subscriptions

Recommended Reading

  1. Bezdek JC, Pal NR. Some new indexes of cluster validity. IEEE Trans Syst Man Cybern Part B. 1998;28(3):301–15.

    Article  Google Scholar 

  2. Datta S, Datta S. Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics. 2003;19(4):459–66.

    Article  Google Scholar 

  3. El-Melegy MT, Zanaty EA, Abd-Elhafiez WM, Farag AA. On cluster validity indexes in fuzzy and hard clustering algorithms for image segmentation. In: Proceedings of International Conference on Image Processing; 2007. p. 5–8.

    Google Scholar 

  4. Halkidi M, Batistakis Y, Vazirgiannis M. On clustering validation techniques. J Intell Inf Syst. 2001;17(2–3):107–45.

    Article  MATH  Google Scholar 

  5. Halkidi M, Gunopulos D, Vazirgiannis M, Kumar N, Domeniconi C. A clustering framework based on subjective and objective validity criteria. ACM Trans Knowl Discov Data. 2008;1(4).

    Google Scholar 

  6. Jiang D, Tang C, Zhang A. Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng. 2004;16(11):1370–86.

    Article  Google Scholar 

  7. Kim M, Ramakrishna RS. New indices for cluster validity assessment. Pattern Recogn Lett. 2005;26(15):2353–63.

    Article  Google Scholar 

  8. Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell. 2002;24(12):1650–4.

    Article  Google Scholar 

  9. NIPS 2005 workshop on theoretical foundations of clustering, Saturday, December 10th, 2005. Available at: http://www.kyb.tuebingen.mpg.de/bs/people/ule/clustering_workshop_nips05/clustering_workshop_nips05.htm_

  10. Pal NR, Bezdek JC. On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst. 1995;3(3):370–9.

    Article  Google Scholar 

  11. Rand WM. Objective criteria for the evaluation of clustering methods. J Am Stat Assoc. 1971;66(336):846–50.

    Article  Google Scholar 

  12. Wang J-S, Chiang J-C. A cluster validity measure with a hybrid parameter search method for the support vector clustering algorithm. Pattern Recog. 2008;41(2):506–20.

    Article  MATH  Google Scholar 

  13. Zhang J, Modestino JW. A model-fitting approach to cluster validation with application to stochastic model-based image segmentation. IEEE Trans Pattern Anal Mach Intell. 1990;12(10):1009–17.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Athens University of Economics & Business, Athens, Greece

    Michalis Vazirgiannis

Authors
  1. Michalis Vazirgiannis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michalis Vazirgiannis .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Department of Computer Science and Engineering, The University of California at Riverside, Bourns College of Engineering, 92521, Riverside, CA, USA

    Dimitrios Gunopulos

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this entry

Cite this entry

Vazirgiannis, M. (2016). Clustering Validity. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_616-2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_616-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation