Multiple Dissimilarity Matrices Research Articles

Clustering of multi-view relational data based on particle swarm optimization

Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clustering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position update step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the importance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views.

Nonlinear multicriteria clustering based on multiple dissimilarity matrices

We present a new algorithm capable of partitioning sets of objects by taking simultaneously into account their relational descriptions given by multiple dissimilarity matrices. The novelty of the algorithm is that it is based on a nonlinear aggregation criterion, weighted Tchebycheff distances, more appropriate than linear combinations (such as weighted averages) for the construction of compromise solutions. We obtain a hard partition of the set of objects, the prototype of each cluster and a weight vector that indicates the relevance of each matrix in each cluster. Since this is a clustering algorithm for relational data, it is compatible with any distance function used to measure the dissimilarity between objects. Results obtained in experiments with data sets (synthetic and real) show the usefulness of the proposed algorithm.

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.

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.

Fuzzy Clustering and Aggregation of Relational Data With Instance-Level Constraints

In this paper, we introduce a semisupervised approach for clustering and aggregating relational data (SS-CARD). We assume that data is available in a relational form, where information only about the degrees to which pairs of objects in the dataset are related is available. Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different features, different mappings, or even different sensors. SS-CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously. These weights have two main advantages. First, they help in partitioning the data into more meaningful clusters. Second, they can be used as part of a more complex learning system to enhance its learning behavior. SS-CARD uses partial supervision information that consists of a small set of constraints on which instances (should link) or ( should not link) reside in the same cluster. This additional information can guide the algorithm in learning the optimal relevance weights and in generating a better partition. The performance of the proposed algorithm is illustrated by using it in two different applications. The first one consists of categorizing the discrete nominal-valued mushroom data. The second application consists of categorizing a collection of images where each image is represented by several continuous features. For both applications, we represent the pairwise image dissimilarities by multiple relational matrices extracted from different feature sets. The results are compared with those obtained by three traditional relational clustering methods. We show that the partial supervision information and the learned aggregation weights can improve the results significantly.

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

In this paper, we introduce a new algorithm for clustering and aggregating relational data (CARD). We assume that data is available in a relational form, where we only have information about the degrees to which pairs of objects in the data set are related. Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different sensors, features, or mappings. CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously. The cluster dependent relevance weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in subsequent steps of a learning system to improve its learning behavior. The performance of the proposed algorithm is illustrated by using it to categorize a collection of 500 color images. We represent the pairwise image dissimilarities by six different relational matrices that encode color, texture, and structure information.