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Fuzzy Optimization and Decision Making

Erschienen in:

09.03.2016

Exponential distance-based fuzzy clustering for interval-valued data

verfasst von: Pierpaolo D’Urso, Riccardo Massari, Livia De Giovanni, Carmela Cappelli

Erschienen in: Fuzzy Optimization and Decision Making | Ausgabe 1/2017

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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.

Vorheriger Artikel An original approach to ranking fuzzy numbers by inclusion index and Bitset Encoding

Nächster Artikel Limit properties and derivative operations in the metric space of intuitionistic fuzzy numbers

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http://www.brace.sinanet.apat.it/web/struttura.html. Data retrieved on 2015-05-03.

Retrieved at http://www1.toronto.ca/City%20Of%20Toronto/Information%20&%20Technology/Open%20Data/Data%20Sets/Assets/Files/E-Bike_Survey_Responses.xls, on 2015-05-03.

Anderson, D. T., Bezdek, J. C., Popescu, M., & Keller, J. M. (2010). Comparing fuzzy, probabilistic, and possibilistic partitions. IEEE Transactions on Fuzzy Systems, 18(5), 906–918.CrossRef

Campello, R. J., & Hruschka, E. R. (2006). A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets and Systems, 157(21), 2858–2875.MathSciNetCrossRefMATH

Cazes, P., Chouakria, A., Diday, E., & Schektrman, Y. (1997). Extension de l’analyse en composantes principales à des données de type intervalle. Revue de Statistique Appliquée, 45(3), 5–24.

Coppi, R., & D’Urso, P. (2002). Fuzzy k-means clustering models for triangular fuzzy time trajectories. Statistical Methods and Applications, 11(1), 21–40.CrossRefMATH

De Carvalho, Fd A T, & Lechevallier, Y. (2009). Partitional clustering algorithms for symbolic interval data based on single adaptive distances. Pattern Recognition, 42(7), 1223–1236.CrossRefMATH

De Carvalho, Fd A T, & Tenório, C. P. (2010). Fuzzy k-means clustering algorithms for interval-valued data based on adaptive quadratic distances. Fuzzy Sets and Systems, 161(23), 2978–2999.MathSciNetCrossRefMATH

De Carvalho, Fd A T, De Souza, R. M., Chavent, M., & Lechevallier, Y. (2006). Adaptive hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters, 27(3), 167–179.CrossRef

Denoeux, T., & Masson, M. (2000). Multidimensional scaling of interval-valued dissimilarity data. Pattern Recognition Letters, 21(1), 83–92.CrossRef

Dey, V., Pratihar, D. K., & Datta, G. L. (2011). Genetic algorithm-tuned entropy-based fuzzy c-means algorithm for obtaining distinct and compact clusters. Fuzzy Optimization and Decision Making, 10(2), 153–166.MathSciNetCrossRef

Duarte Silva, A. P., & Brito, P. (2015). Discriminant analysis of interval data: An assessment of parametric and distance-based approaches. Journal of Classification, 32(3), 516–541. doi:10.1007/s00357-015-9189-8.MathSciNetCrossRefMATH

D’Urso, P., & De Giovanni, L. (2014). Robust clustering of imprecise data. Chemometrics and Intelligent Laboratory Systems, 136, 58–80.CrossRef

D’Urso, P., & Giordani, P. (2004). A least squares approach to principal component analysis for interval valued data. Chemometrics and Intelligent Laboratory Systems, 70(2), 179–192.MathSciNetCrossRef

D’Urso, P., & Giordani, P. (2006). A robust fuzzy k-means clustering model for interval valued data. Computational Statistics, 21(2), 251–269.MathSciNetCrossRefMATH

D’Urso, P., De Giovanni, L., & Massari, R. (2015a). Time series clustering by a robust autoregressive metric with application to air pollution. Chemometrics and Intelligent Laboratory Systems, 141, 107–124.CrossRef

D’Urso, P., De Giovanni, L., & Massari, R. (2015b). Trimmed fuzzy clustering for interval-valued data. Advances in Data Analysis and Classification, 9(1), 21–40.MathSciNetCrossRef

García-Escudero, L. A., & Gordaliza, A. (2005). A proposal for robust curve clustering. Journal of Classification, 22(2), 185–201.MathSciNetCrossRefMATH

Giordani, P., & Kiers, H. A. (2004). Three-way component analysis of interval-valued data. Journal of Chemometrics, 18(5), 253–264.CrossRef

Gowda, K. C., & Diday, E. (1991). Symbolic clustering using a new dissimilarity measure. Pattern Recognition, 24(6), 567–578.CrossRef

Guru, D. S., Kiranagi, B. B., & Nagabhushan, P. (2004). Multivalued type proximity measure and concept of mutual similarity value useful for clustering symbolic patterns. Pattern Recognition Letters, 25(10), 1203–1213.CrossRef

Hung, T. W. (2007). The bi-objective fuzzy c-means cluster analysis for tsk fuzzy system identification. Fuzzy Optimization and Decision Making, 6(1), 51–61.MathSciNetCrossRefMATH

Kim, J., Krishnapuram, R., & Davé, R. (1996). Application of the least trimmed squares technique to prototype-based clustering. Pattern Recognition Letters, 17(6), 633–641.CrossRef

Krishnapuram, R., Joshi, A., Nasraoui, O., & Yi, L. (2001). Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems, 9(4), 595–607.CrossRef

Leite, D., Ballini, R., Costa, P., & Gomide, F. (2012). Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evolving Systems, 3(2), 65–79.CrossRef

Wu, K. L., & Yang, M. S. (2002). Alternative c-means clustering algorithms. Pattern Recognition, 35(10), 2267–2278.CrossRefMATH

Xu, Z. (2012). Fuzzy ordered distance measures. Fuzzy Optimization and Decision Making, 11(1), 73–97.MathSciNetCrossRefMATH

Titel: Exponential distance-based fuzzy clustering for interval-valued data
verfasst von: Pierpaolo D’Urso
Riccardo Massari
Livia De Giovanni
Carmela Cappelli
Publikationsdatum: 09.03.2016
Verlag: Springer US
Erschienen in: Fuzzy Optimization and Decision Making / Ausgabe 1/2017
Print ISSN: 1568-4539
Elektronische ISSN: 1573-2908
DOI: https://doi.org/10.1007/s10700-016-9238-8

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