Making many-to-many parallel coordinate plots scalable by asymmetric biclustering

Hsiang Yun Wu, Yusuke Niibe, Kazuho Watanabe, Shigeo Takahashi, Makoto Uemura, Issei Fujishiro

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension variables or subsets of the data samples, is required to describe the essence hidden in the original dataset. This paper proposes a visual data mining framework for supporting semiautomatic data analysis that builds upon asymmetric biclustering to explore highly correlated feature subspaces. For this purpose, a variant of parallel coordinate plots, many-to-many parallel coordinate plots, is extended to visually assist appropriate selections of feature subspaces as well as to avoid intrinsic visual clutter. In this framework, biclustering is applied to dimension variables and data samples of the dataset simultaneously and asymmetrically. A set of variable axes are projected to a single composite axis while data samples between two consecutive variable axes are bundled using polygonal strips. This makes the visualization method scalable and enables it to play a key role in the framework. The effectiveness of the proposed framework has been empirically proven, and it is remarkably useful for many-to-many parallel coordinate plots.

本文言語English
ホスト出版物のタイトル2017 IEEE Pacific Visualization Symposium, PacificVis 2017 - Proceedings
編集者Yingcai Wu, Daniel Weiskopf, Tim Dwyer
出版社IEEE Computer Society
ページ305-309
ページ数5
ISBN(電子版)9781509057382
DOI
出版ステータスPublished - 2017 9 11
イベント10th IEEE Pacific Visualization Symposium, PacificVis 2017 - Seoul, Korea, Republic of
継続期間: 2017 4 182017 4 21

出版物シリーズ

名前IEEE Pacific Visualization Symposium
ISSN(印刷版)2165-8765
ISSN(電子版)2165-8773

Other

Other10th IEEE Pacific Visualization Symposium, PacificVis 2017
国/地域Korea, Republic of
CitySeoul
Period17/4/1817/4/21

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ
  • ソフトウェア

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