Exponential distance-based fuzzy clustering for interval-valued data

Pierpaolo D’Urso (), Riccardo Massari, Livia De Giovanni and Carmela Cappelli
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Pierpaolo D’Urso: Sapienza University of Rome
Riccardo Massari: Sapienza University of Rome
Livia De Giovanni: LUISS Guido Carli
Carmela Cappelli: Università Federico II di Napoli

Fuzzy Optimization and Decision Making, 2017, vol. 16, issue 1, No 3, 70 pages

Abstract: Abstract In several real life and research situations data are collected in the form of intervals, the so called interval-valued data. In this paper a fuzzy clustering method to analyse interval-valued data is presented. In particular, we address the problem of interval-valued data corrupted by outliers and noise. In order to cope with the presence of outliers we propose to employ a robust metric based on the exponential distance in the framework of the Fuzzy C-medoids clustering mode, the Fuzzy C-medoids clustering model for interval-valued data with exponential distance. The exponential distance assigns small weights to outliers and larger weights to those points that are more compact in the data set, thus neutralizing the effect of the presence of anomalous interval-valued data. Simulation results pertaining to the behaviour of the proposed approach as well as two empirical applications are provided in order to illustrate the practical usefulness of the proposed method.

Keywords: Interval-valued data; Outlier interval data; Fuzzy C-medoids clustering; Exponential distance; Robust clustering (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10700-016-9238-8

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