Visualization of data structures by fuzzy clustering of graphs

Seiji Hotta, Kiichi Urahama

Research output: Contribution to journalArticle

Abstract

A visualization method is presented for data represented by a graph partitioned into fuzzy clusters. The data are arranged in a two- or three-dimensional space by correspondence analysis based on the memberships of the data in the obtained clusters. Data related by their links are represented by a directed graph or a bipartite undirected one. The fuzzy clusters are then extracted sequentially from the data based on this graph representation. At each stage of cluster extraction, memberships are calculated by solving an optimization problem using an iterative scheme. The data are then arranged in a two- or a three-dimensional space by correspondence analysis based on the memberships of the data. This data visualization method can be used to recommend movies by visual collaborative filtering and to visualize structures in a software program. It can be extended to more complexly structured data represented by the combination of a directed graph and a bipartite undirected one. This extended method can be used to search for Web pages by keywords and to recommend them as links. For example, the Web can be browsed to find information about sofas and carpets based on the impression they make and to visualy support their coordination.

Original languageEnglish
Pages (from-to)1748-1755
Number of pages8
JournalKyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
Volume54
Issue number12
DOIs
Publication statusPublished - Jan 1 2000

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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