Partitioning hard 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, BP 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:
Pattern Recognition
Year:
2012

Citing 15
Cited 2

BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A monothetic clustering method

Pattern Recognition Letters
Data clustering: a review

ACM Computing Surveys (CSUR)
ROCK: a robust clustering algorithm for categorical attributes

Information Systems
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry

IEEE Transactions on Pattern Analysis and Machine Intelligence
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

Data Mining and Knowledge Discovery
Chameleon: Hierarchical Clustering Using Dynamic Modeling

Computer
An Efficient k-Means Clustering Algorithm: Analysis and Implementation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaborative fuzzy clustering

Pattern Recognition Letters
An Efficient Fuzzy C-Means Clustering Algorithm

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Clustering and aggregation of relational data with applications to image database categorization

Pattern Recognition
Generalized fuzzy c-means clustering strategies using Lp norm distances

IEEE Transactions on Fuzzy Systems
Reducing the time complexity of the fuzzy c-means algorithm

IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms

IEEE Transactions on Neural Networks

An architecture to efficiently learn co-similarities from multi-view datasets

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Nonlinear multicriteria clustering based on multiple dissimilarity matrices

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper introduces hard clustering algorithms that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. These matrices have been generated using different sets of variables and dissimilarity functions. These methods are designed to furnish a partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These relevance weights change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Experiments with data sets (synthetic and from UCI machine learning repository) described by real-valued variables as well as with time trajectory data sets show the usefulness of the proposed algorithms.