Robust fuzzy clustering models for fuzzy data are proposed. In particular, using a “Partitioning Around Medoids” (PAM) approach, first a timid robustification of fuzzy clustering for a general class of fuzzy data is proposed. Successively, we propose three robust fuzzy clustering models based on, respectively, the so-called metric, noise and trimmed approaches. The metric approach achieves its robustness with respect to outliers by taking into account a “robust” distance measure, the noise approach by introducing a noise cluster represented by a noise prototype, and the trimmed approach by trimming away a certain fraction of data units. A comparative simulation study and measures of misclassification and of robustness with respect to prototype detection in the presence of outliers have been developed. Several applications to chemometrical and benchmark data are also presented.

Robust clustering of imprecise data / D'Urso, P.; DE GIOVANNI, Livia. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 136:(2014), pp. 58-80. [10.1016/j.chemolab.2014.05.004]

Robust clustering of imprecise data

DE GIOVANNI, LIVIA
2014

Abstract

Robust fuzzy clustering models for fuzzy data are proposed. In particular, using a “Partitioning Around Medoids” (PAM) approach, first a timid robustification of fuzzy clustering for a general class of fuzzy data is proposed. Successively, we propose three robust fuzzy clustering models based on, respectively, the so-called metric, noise and trimmed approaches. The metric approach achieves its robustness with respect to outliers by taking into account a “robust” distance measure, the noise approach by introducing a noise cluster represented by a noise prototype, and the trimmed approach by trimming away a certain fraction of data units. A comparative simulation study and measures of misclassification and of robustness with respect to prototype detection in the presence of outliers have been developed. Several applications to chemometrical and benchmark data are also presented.
Fuzzy k-Medoids clustering, Fuzzy data, Robust fuzzy clustering, Exponential distance, Noise cluster, Trimming, Chemical data, Ecotoxicological data
Robust clustering of imprecise data / D'Urso, P.; DE GIOVANNI, Livia. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 136:(2014), pp. 58-80. [10.1016/j.chemolab.2014.05.004]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11385/135593
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