Softwares

Partition's Visualizer (PVis)

Recent advances in cluster analysis highlight the importance of finding multiple meaningful partitions and point out to the need for approaches to evaluate them. They also suggest that the evaluation should consider knowledge of a domain expert. We present a visualization method, called PVis (Partition's Visualizer), that allows the integrated visualization of a collection of partitions. PVis allows to compare the content of a set of partitions. The comparison can be done with respect to priori knowledge provided by an expert. PVis can be useful in the discovery of relevant information to the domain experts performing cluster analysis.

• http://lasid.sor.ufscar.br/pvisproject

If you publish material using PVis, please cite the following reference:

• Katti Faceli, Tiemi C. Sakata, André de Carvalho, and Marcílio C. P. de Souto. PVis - partitions’ visualizer: Extracting knowledge by visualizing a collection of partitions. In 2014 International Joint Conference on Neural Networks, IJCNN, pages 3056–3061, 2014. DOI: 10.1109/IJCNN.2014.6889672

Multi-Objective CLustering Ensemble (MOCLE)

MOCLE is a framework for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. It can deal with datasets presenting different types of clusters. MOCLE's general functioning consists in building a collection of base partitions by considering different clustering criteria and optimizing this collection with multiple criteria to produce a set of consensus partitions. The result is a concise set of partitions representing alternative trade-offs among the objective functions.

• http://lasid.sor.ufscar.br/mocleproject/

If you publish material using MOCLE, please cite the following reference:

• Katti Faceli, Marcílio C. P. de Souto, Daniel S. A. de Araujo, and André de Carvalho. Multi-objective clustering ensemble for gene expression data analysis. Neurocomputing, 72(13-15):2763–2774, 2009. DOI: 10.1016