Katti Faceli

Multi-Objective Clustering Ensemble

In this paper, we present an algorithm for cluster analysis that provides a robust way to deal with datasets presenting different types of clusters and allows finding more than one structure in a dataset. Our approach is based on ideas from cluster ensembles and multi-objective clustering. We apply a Pareto-based multi-objective genetic algorithm with a special crossover operator. Such an operator combines a number of partitions obtained according to different clustering criteria. As a result, our approach generates a concise and stable set of partitions representing different trade-offs between two validation measures related to different clustering criteria.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. Its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective clustering with automatic K-determination (MOCK), the algorithm most closely related to ours.

Combining intelligent techniques for sensor fusion

Mobile robots rely on sensor data to build a representation of their environment. However, sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of Artificial Intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of the distance measure between a robot and an object in its work environment, based on measures obtained from different sensors. Several Machine Learning algorithms are investigated to fuse the sensors data. The best model generated by each algorithm is called estimator. It is shown that the employment of estimators based on Artificial Intelligence can improve significantly the performance achieved by each sensor alone. The Machine Learning algorithms employed have different characteristics, causing the estimators to have different behaviors in different situations. Aiming to achieve an even more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can further improve the reliability and accuracy of the distances measured by the individual sensors and estimators used for sensor fusion.

Technologies and decision support systems to aid solid-waste management: a systematic review.

Population growth associated with population migration to urban areas and industrial development have led to a consumption relation that results in environmental, social, and economic problems. With respect to the environment, a critical concern is the lack of control and the inadequate management of the solid waste generated in urban centers. Among the challenges are proper waste-collection management, treatment, and disposal, with an emphasis on sustainable management. This paper presents a systematic review on scientific publications concerning decision support systems applied to Solid Waste Management (SWM) using ICTs and OR in the period of 2010-2013. A statistical analysis of the eighty-seven most relevant publications is presented, encompassing the ICTs and OR methods adopted in SWM, the processes of solid-waste management where they were adopted, and which countries are investigating solutions for the management of solid waste. A detailed discussion on how the ICTs and OR methods have been combined in the solutions was also presented. The analysis and discussion provided aims to help researchers and managers to gather insights on technologies/methods suitable the SWM challenges they have at hand, and on gaps that can be explored regarding technologies/methods that could be useful as well as the processes in SWM that currently do not benefit from using ICTs and OR methods.

Multi-objective clustering ensemble with prior knowledge

In this paper, we introduce an approach to integrate prior knowledge in cluster analysis, which is different from the existing ones for semi-supervised clustering methods. In order to aid the discovery of alternative structures present in the data, we consider the knowledge of some existing complete classification of such data. The approach proposed is based on our Multi-Objective Clustering Ensemble algorithm (MOCLE). This algorithm generates a concise and stable set of partitions, which represents different trade-offs between several measures of partition quality. The prior knowledge is automatically integrated in MOCLE by embedding it into one of the objective functions. In this case, the function gives as output the quality of a partition, considering the prior knowledge of one of the known structures of the data.

A Strategy for the Selection of Solutions of the Pareto Front Approximation in Multi-objective Clustering Approaches

One of the advantages of Pareto-based multi-objective genetic algorithms for clustering, when compared to classical clustering algorithms, is that, instead of a single solution (partition), they give as an output a set of solutions (approximation of the Pareto front or Pareto front, for short). However, such a set could be very large (e.g., hundreds of partitions) and, consequently, difficult to be analyzed manually. We present a selection strategy, based on the corrected Rand index, that aims at recommending, as final solution for Pareto-based multi-objective genetic algorithm approaches, a subset of partitions from the Pareto front. This subset should be much smaller than the the latter and, at the same time, keep the quality and the diversity of the partitions. In order to test our strategy, we develop a study of case in which we apply the strategy to the sets of solutions obtained with the multi-objective clustering ensemble algorithm (MOCLE) in the context of several data sets.

Data clustering based on complex network community detection

Data clustering is an important technique to extract and understand relevant information in large data sets. In this paper, a clustering algorithm based on graph theoretic models and community detection in complex networks is proposed. Two steps are involved in this processing: The first step is to represent input data as a network and the second one is to partition the network into subnetworks producing data clusters. In the network partition stage, each node has a randomly assigned initial angle and it is gradually updated according to its neighbors angle agreement. Finally, a stable state is reached and nodes belonging to the same cluster have similar angles. This process is repeated, each time a cluster is chosen and results in an hierarchical divisive clustering. Simulation results show two main advantages of the algorithm: the ability to detect clusters in different shapes, densities and sizes and the ability to generate clusters with different refinement degrees. Besides of these, the proposed algorithm presents high robustness and efficiency in clustering.

Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming

This paper investigates a genetic programming (GP) approach aimed at the multi-objective design of hierarchical consensus functions for clustering ensembles. By this means, data partitions obtained via different clustering techniques can be continuously refined (via selection and merging) by a population of fusion hierarchies having complementary validation indices as objective functions. To assess the potential of the novel framework in terms of efficiency and effectiveness, a series of systematic experiments, involving eleven variants of the proposed GP-based algorithm and a comparison with basic as well as advanced clustering methods (of which some are clustering ensembles and/or multi-objective in nature), have been conducted on a number of artificial, benchmark and bioinformatics datasets. Overall, the results corroborate the perspective that having fusion hierarchies operating on well-chosen subsets of data partitions is a fine strategy that may yield significant gains in terms of clustering robustness.

Letters: Inducing multi-objective clustering ensembles with genetic programming

The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different hierarchical clustering ensembles be simultaneously evolved taking into account complementary validity indices. Results of computational experiments conducted with artificial and real datasets indicate that, in most of the cases, at least one of the Pareto optimal partitions returned by the proposed approach compares favorably or go in par with the consensual partitions yielded by two well-known clustering ensemble methods in terms of clustering quality, as gauged by the corrected Rand index.

A Comparison of External Clustering Evaluation Indices in the Context of Imbalanced Data Sets

For highly imbalanced data sets, almost all the instances are labeled as one class, whereas far fewer examples are labeled as the other classes. In this paper, we present an empirical comparison of seven different clustering evaluation indices when used to assess partitions generated from highly imbalanced data sets. Some of the metrics are based on matching of sets (F-measure), information theory (normalized mutual information and adjusted mutual information), and pair of objects counting (Rand and adjusted Rand indices). We also investigate the BCubed metric, which takes into account the concepts of recall, precision, as well as counting pairs. Furthermore, in order to avoid the class size imbalance effect, we propose a modification to the Rand index, referred to as the normalized class size Rand (NCR) index. In terms of results, apart from NCR, our experiments indicate that all the other analyzed indices are not able to deal properly with the problem of class size imbalance.