Filipe M. de Melo

Partitioning hard clustering algorithms based on multiple dissimilarity matrices

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.

Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices

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.

A multi-view relational fuzzy c-medoid vectors clustering algorithm

This paper gives a multi-view relational fuzzy c-medoid vectors clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus fuzzy partition. These matrices could have been obtained using different sets of variables and dissimilarity functions. This algorithm is designed to give a fuzzy partition and a vector of medoids for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each iteration of the algorithm and are different from one cluster to another. Moreover, various tools for interpreting the fuzzy partition and fuzzy clusters provided by this algorithm are also presented. Several examples illustrate the performance and usefulness of the proposed algorithm.

A Fuzzy C-Medoids Clustering Algorithm Based on Multiple Dissimilarity Matrices

This paper gives a relational fuzzy c-medoids clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices could have been obtained using different sets of variables and dissimilarity functions. This algorithm is designed to give a fuzzy partition and a prototype for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each algorithms iteration and are different from one cluster to another. Several examples illustrate the usefulness of the proposed algorithm.

Semi-supervised fuzzy c-medoids clustering algorithm with multiple prototype representation

Semi-supervised clustering is a special form of classification that uses a large amount of unlabeled data together with labeled data to achieve better classification results. This paper introduces a semi-supervised fuzzy clustering algorithm of relational data with multiple prototype representation (SS-CLAMP) that aims to furnish a partition and a set of prototypes 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 in a competitive way and that takes into account pairwise constraints must-link and cannot-link. Experiments with real-valued data sets show the usefulness of the proposed algorithm.

Multi-view Clustering on Relational Data

Clustering is a popular task in knowledge discovery. In this chapter we illustrate this fact with a new clustering algorithm that is able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The advantages of this algorithm are threefold: it uses any dissimilarities between objects, it automatically ponderates the impact of each dissimilarity matrice and it provides interpretation tools.We illustrate the usefulness of this clustering method with two experiments. The first one uses a data set concerning handwritten numbers (digitized pictures) that must be recognized. The second uses a set of reports for which we have an expert classification given a priori so we can compare this classification with the one obtained automatically.

A relational fuzzy c-means clustering algorithm based on multiple dissimilarity matrices

This paper introduces a relational fuzzy c-means clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices could have been obtained using different sets of variables and dissimilarity functions. This algorithm is designed to give a fuzzy partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each algorithms iteration and are different from one cluster to another. Experiments with datasets from UCI machine learning repository show the usefulness of the proposed algorithm.