A similarity-based robust clustering method

IEEE Transactions on Pattern Analysis and Machine Intelligence
Miin Shen Yang, Kuo-Lung Wu

Abstract

This paper presents an alternating optimization clustering procedure called a similarity-based clustering method (SCM). It is an effective and robust approach to clustering on the basis of a total similarity objective function related to the approximate density shape estimation. We show that the data points in SCM can self-organize local optimal cluster number and volumes without using cluster validity functions or a variance-covariance matrix. The proposed clustering method is also robust to noise and outliers based on the influence function and gross error sensitivity analysis. Therefore, SCM exhibits three robust clustering characteristics: 1) robust to the initialization (cluster number and initial guesses), 2) robust to cluster volumes (ability to detect different volumes of clusters), and 3) robust to noise and outliers. Several numerical data sets and actual data are used in the SCM to show these good aspects. The computational complexity of SCM is also analyzed. Some experimental results of comparing the proposed SCM with the existing methods show the superiority of the SCM method.

References

Feb 1, 1979·IEEE Transactions on Pattern Analysis and Machine Intelligence·D L Davies, D W Bouldin

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Citations

Oct 11, 2005·Neural Computation·Qing Song
Oct 17, 2008·Computer Methods and Programs in Biomedicine·Piotr M SzczypińskiArtur Klepaczko
Sep 30, 2014·IEEE Transactions on Neural Networks and Learning Systems·Huaping LiuFuchun Sun
Nov 15, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kazunori Iwata, Akira Hayashi
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Jun 19, 2008·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Miin-Shen YangJian Yu
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Mar 10, 2012·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Yu-Teng ChangDimitrios Pantazis
Jan 4, 2017·Computational Intelligence and Neuroscience·Min RenJing Yi
Nov 27, 2018·Technology and Health Care : Official Journal of the European Society for Engineering and Medicine·H Ozge BacakMehmet Sinan Beksac

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