Publication 2004 Issue No. 4 - April Abstract - A Similarity-Based Robust Clustering Method
A Similarity-Based Robust Clustering Method
April 2004 (vol. 26 no. 4)
pp. 434-448
 ASCII Text x Miin-Shen Yang, Kuo-Lung Wu, "A Similarity-Based Robust Clustering Method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 4, pp. 434-448, April, 2004.
 BibTex x @article{ 10.1109/TPAMI.2004.1265860,author = {Miin-Shen Yang and Kuo-Lung Wu},title = {A Similarity-Based Robust Clustering Method},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {26},number = {4},issn = {0162-8828},year = {2004},pages = {434-448},doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.1265860},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - A Similarity-Based Robust Clustering MethodIS - 4SN - 0162-8828SP434EP448EPD - 434-448A1 - Miin-Shen Yang, A1 - Kuo-Lung Wu, PY - 2004KW - Robust clustering algorithmKW - fuzzy clusteringKW - alternating optimization algorithmKW - total similarityKW - noise.VL - 26JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -

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.

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Index Terms:
Robust clustering algorithm, fuzzy clustering, alternating optimization algorithm, total similarity, noise.
Citation:
Miin-Shen Yang, Kuo-Lung Wu, "A Similarity-Based Robust Clustering Method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 4, pp. 434-448, April 2004, doi:10.1109/TPAMI.2004.1265860