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JACIII Vol.22 No.5 pp. 585-592
doi: 10.20965/jaciii.2018.p0585
(2018)

Paper:

Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering

Katsuhiro Honda, Takuya Sako, Seiki Ubukata, and Akira Notsu

Osaka Prefecture University
1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

Received:
March 13, 2018
Accepted:
April 16, 2018
Published:
September 20, 2018
Keywords:
co-clustering, data structure visualization, eigen-problem, cluster validation
Abstract
Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering

Visual image of co-cluster structure

Co-cluster extraction is a basic approach for summarization of cooccurrence information. This paper proposes a visual assessment technique for co-cluster structure analysis through cooccurrence-sensitive ordering, which realizes the hybrid concept of the coVAT algorithm and distance-sensitive ordering in relational data clustering. Object-item cooccurrence information is first enlarged into an (object + item) × (object + item) cooccurrence data matrix, and then, cooccurrence-sensitive ordering is performed through spectral ordering of the enlarged matrix. Additionally, this paper also consider the intuitive validation of co-cluster structures considering cluster crossing curves, which was adopted in cluster validation with distance-sensitive ordering. The characteristic features of the proposed approach are demonstrated through several numerical experiments including application to social analysis of Japanese prefectural statistics.

Cite this article as:
K. Honda, T. Sako, S. Ubukata, and A. Notsu, “Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 585-592, 2018.
Data files:
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Last updated on Oct. 23, 2018