DocumentCode :
1797896
Title :
PVis — Partitions´ visualizer: Extracting knowledge by visualizing a collection of partitions
Author :
Faceli, Katti ; Sakata, Tiemi C. ; de Carvalho, Andre C. P. L. F. ; de Souto, Marcilio C. P.
Author_Institution :
Dept. de Computocao de Sorocaba, Univ. Fed. de Sao Carlos, Sorocaba, Brazil
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
3056
Lastpage :
3061
Abstract :
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. In this paper, we present a visualization method, called PVis1 (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. In order to illustrate our approach, we give an example of how to perform an exploratory analysis of collections of partitions. In order to do so, we use a well-known dataset from the Bioinformatics domain, regarding molecular classification of cancer.
Keywords :
bioinformatics; data visualisation; knowledge acquisition; pattern clustering; PVi; bioinformatics domain; cancer molecular classification; cluster analysis; integrated visualization method; knowledge extraction; partition visualizer; Bioinformatics; Cancer; Clustering algorithms; Color; Data visualization; Image color analysis; Partitioning algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889672
Filename :
6889672
Link To Document :
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