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JACIII Vol.24 No.1 pp. 65-72
doi: 10.20965/jaciii.2020.p0065
(2020)

Paper:

Visualization of Potential Technical Solutions by SOM and Co-Clustering and its Extension to Multi-View Situation

Yasushi Nishida and Katsuhiro Honda

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

Received:
May 19, 2019
Accepted:
September 12, 2019
Published:
January 20, 2020
Keywords:
patent documents, technical solution, self-organizing maps, co-clustering, multi-view clustering
Abstract
Visualization of Potential Technical Solutions by SOM and Co-Clustering and its Extension to Multi-View Situation

SOM visualization of technical term connections

In order to support inspiration of potential technical solutions, this paper considers visualization of solving means varied in patent documents through SOM. Non-structured patent document data can be quantified through two different scheme: word level co-occurrence probability vectors and correlation coefficients of the generated co-occurrence probability vectors. Comparing the two SOMs derived with the above schemes is useful for supporting innovation acceleration through extraction of important pairs of related factors in new technology development. In this paper, co-cluster structures are utilized for emphasizing field-related solutions by constructing multiple SOMs after co-clustering. Document × keyword co-occurrence analysis achieves extraction of co-clusters consisting of mutually related pairs in particular fields. Additionally, this paper also considers an extension to a multi-view situation, where each patent is characterized by additional patent classification system of F-term by Japan Patent Office. Through multi-view co-clustering among documents × keywords × F-terms, theme field-related knowledge is demonstrated to be extracted.

Cite this article as:
Y. Nishida and K. Honda, “Visualization of Potential Technical Solutions by SOM and Co-Clustering and its Extension to Multi-View Situation,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.1, pp. 65-72, 2020.
Data files:
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Last updated on Nov. 26, 2020