ABSTRACT
Validation of clustering results is an important issue in the context of machine learning research and it is essential for the success of clustering applications. Choosing the appropriate validation index for evaluating the results of a particular clustering algorithm remains a challenge. The quality of partitions generated by different clustering algorithms can be evaluated using different indices based on external or internal criteria. In this paper, we have proposed a methodology for selecting the most suitable cluster validation internal index, relating external and internal criteria through a regression model applied on the results of partitioning clustering algorithm.
- D. L. Davies and D. W. Bouldin. A cluster separation measure. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (2):224--227, 1979. Google ScholarDigital Library
- M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques. J. Intell. Inf. Syst., 17(2-3):107--145, Dec. 2001. Google ScholarDigital Library
- J. Han, M. Kamber, and J. Pei. Data mining, southeast asia edition: Concepts and techniques. Morgan kaufmann, 2006. Google ScholarDigital Library
- J. Handl, J. Knowles, and D. B. Kell. Computational cluster validation in post-genomic data analysis. Bioinformatics, 21(15):3201--3212, 2005. Google ScholarDigital Library
- J. A. Hartigan and M. A. Wong. Algorithm as 136: A k-means clustering algorithm. Applied statistics, pages 100--108, 1979.Google Scholar
- G. Kou, Y. Peng, and G. Wang. Evaluation of clustering algorithms for financial risk analysis using mcdm methods. Information Sciences, 275:1--12, 2014.Google ScholarCross Ref
- Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu. Understanding of internal clustering validation measures. In Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM '10, pages 911--916, Washington, DC, USA, 2010. Google ScholarDigital Library
- J. R. Quinlan et al. Learning with continuous classes. In 5th Australian joint conference on artificial intelligence, volume 92, pages 343--348. Singapore, 1992.Google Scholar
- L. Vendramin, R. J. Campello, and E. R. Hruschka. Relative clustering validity criteria: A comparative overview. Statistical Analysis and Data Mining, 3(4):209--235, 2010. Google ScholarDigital Library
- R. Xu and D. Wunsch. Clustering. piscataway, 2009. Google ScholarDigital Library
Index Terms
- A methodology for selecting the most suitable cluster validation internal indices
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