• 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