Now showing items 1-10 of 93
A Method to Improve the Analysis of Cluster Ensembles
(Sociedad Iberoamericana de Inteligencia Artificial, 2014-03)
Clustering is fundamental to understand the structure of data. In the past decade the cluster ensembleproblem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a singleconsensus ...
Diversity control for improving the analysis of consensus clustering
(Elsevier Science Inc, 2016-09)
Consensus clustering has emerged as a powerful technique for obtaining better clustering results, where a set of data partitions (ensemble) are generated, which are then combined to obtain a consolidated solution (consensus ...
Análise comparativa de técnicas avançadas de agrupamento
(Universidade Federal de São CarlosUFSCarPrograma de Pós-graduação em Ciência da Computação (Campus SOROCABA)Câmpus Sorocaba, 2016-01-29)
The goal of this study is to investigate the characteristics of the new data clustering approaches, carrying out a comparative study of clustering techniques that combine or select multiple solutions, analyzing these latest ...
Using metaheuristics to optimize the combination of classifier and cluster ensembles
(IOS PressAmsterdam, 2015)
We investigate how to make a simpler version of an existing algorithm, named 'C POT. 3'E, from Consensus between Classification and Clustering Ensembles, more user-friendly by automatically tuning its main parameters with ...
A differential evolution algorithm to optimise the combination of classifier and cluster ensembles
(Inderscience EnterprisesGeneva, 2015)
Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, this paper reports on a study on how to make ...
Multi-objective clustering ensemble for gene expression data analysis
(ELSEVIER SCIENCE BV, 2009)
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 ...
Clustering ensembles and space discretization - A new regard toward diversity and consensus
(Elsevier Science BvAmsterdamHolanda, 2010)
Combining classification and clustering for tweet sentiment analysis
(Universidade de São Paulo - USPUniversidade Federal de São Carlos - UFSCarCentro de Robótica de São Carlos - CROBSociedade Brasileira de Computação - SBCSociedade Brasileira de Automática - SBASão Carlos, 2014-10)
The goal of sentiment analysis is to determine opinions, emotions, and attitudes presented in source material. In tweet sentiment analysis, opinions in messages can be typically categorized as positive or negative. To ...
Avaliação do impacto da seleção de partições base em ensemble multiobjetivo
(Universidade Federal de São CarlosUFSCarPrograma de Pós-graduação em Ciência da Computação (Campus SOROCABA)Câmpus Sorocaba, 2018-02-23)
Unsupervised data clustering is not a trivial process, as no previous knowledge is available and real data is often complex and multi-faceted. To make matters worse, traditionally, clustering aims to describe the data ...