Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices

Authors:
Francisco De A. T. De Carvalho;Yves Lechevallier;Filipe M. De Melo
Affiliations:
Centro de Informática, Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, s/n - Cidade Universitária, CEP 50740-540, Recife (PE), Brazil;INRIA-Institut National de Recherche en Informatique et en Automatique Domaine de Voluceau-Rocquencourt B.P. 105, 78153 Le Chesnay Cedex, France;Centro de Informática, Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, s/n - Cidade Universitária, CEP 50740-540, Recife (PE), Brazil
Venue:
Fuzzy Sets and Systems
Year:
2013

Citing 12
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data

Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Collaborative fuzzy clustering

Pattern Recognition Letters
Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics)

Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics)
Fuzzy c-means clustering methods for symbolic interval data

Pattern Recognition Letters
Clustering and aggregation of relational data with applications to image database categorization

Pattern Recognition
Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software
Dynamic clustering of interval data using a Wasserstein-based distance

Pattern Recognition Letters
Unsupervised pattern recognition models for mixed feature-type symbolic data

Pattern Recognition Letters
Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances

Fuzzy Sets and Systems
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.20

Visualization

Abstract

This paper introduces fuzzy clustering algorithms that can partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices to get a final consensus partition. These matrices can be obtained using different sets of variables and dissimilarity functions. These algorithms are designed to furnish a partition and a prototype for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fit between the fuzzy clusters and their representatives. These relevance weights change at each algorithm iteration and can either be the same for all fuzzy clusters or different from one fuzzy cluster to another. Experiments with real-valued data sets from the UCI Machine Learning Repository as well as with interval-valued and histogram-valued data sets show the usefulness of the proposed fuzzy clustering algorithms.