A Review of Clustering and Clustering Quality Measurement
Main Article Content
Abstract
This paper presents a comparative study on clustering methods and developments made at various times. Clustering is defined as unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering objects such as hierarchical, partitioned, grid, density based and model-based. Many algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input parameters. Therefore it is essential to evaluate the result of the clustering algorithm. It is difficult to define whether a clustering result is acceptable or not; thus several clustering validity techniques and indices have been developed. Cluster validity indices are used for measuring the goodness of a clustering result comparing to other ones which were created by other clustering algorithms, or by the same algorithms but using different parameter values. The results of a clustering algorithm on the same data set can vary as the input parameters of an algorithm can extremely modify the behaviour and execution of the algorithm the intention of this paper is to describe the clustering process with an overview of different clustering methods and analysis of clustering validity indices.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
J. Han , M. Kamber , J. Pei , Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2011 .
A . Nagpal , A . Jatain , D. Gaur , Review based on data clustering algorithms, in: Proceedings of the IEEE Conference on Information and Communication Technologies, 2013 .
G.P. Zhang , Neural networks for classification: a survey, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 30 (4) (2002) 451–462 .
A.K. Jain , Data clustering: 50 years beyond k -means, Pattern Recognit. Lett. 31 (8) (2010) 651–666 .
F.S. Marzano , D. Scaranari , G. Vulpiani , Supervised fuzzy-logic classification of hydrometeors using C-band weather radars, IEEE Trans. Geosci. Remote Sens. 45 (11) (2007) 3784–3799 .
Guha, S, Rastogi, R., and Shim K. . ROCK: A Robust Clustering Algorithm for Categorical Attributes. In Proceedings of the IEEE Conference on Data Engineering, (1999)
Rezaee, R., Lelieveldt, B.P.F., and Reiber, J.H.C. (1998). A New Cluster Validity Index for the Fuzzy c-Mean. Pattern Recognition Letters, 19, 237–246.
M. Halkidi, Y. Batistakis and M. Vazirgiannis: On Clustering Validation Techniques, Journal of Intelligent Information Systems, Vol. 17, No. 2-3, pp. 107-145, 2001
Xie, X.L. and Beni, G. (1991). A Validity Measure for Fuzzy Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4), 841–846.
M. Halkidi and M. Vazirgiannis and Y. Batistakis: Quality Scheme Assessment in the Clustering Process, Proc. Of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 265-276, 2000.