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Iterative File Classification Algorithm using Multidimensional Tree

発表形態:
原著論文
主要業績:
その他
単著・共著:
共著
発表年月:
2012年12月
DOI:
会議属性:
国際会議(国内開催を含む)
査読:
有り
リンク情報:

日本語フィールド

著者:
Tetsuro Kakeshita, Shota Yamaguchi
題名:
Iterative File Classification Algorithm using Multidimensional Tree
発表情報:
International Conference on Convergence Content (ICCC 2012), Saga, Japan, December 14-17, 2012.
キーワード:
File classification, multidimensional tree, OLAP (online analytical processing), file server
概要:
抄録:
We have proposed the notion of multidimensional classification to systematically arrange digital files. The method utilizes multidimensional tree each of which represents independent viewpoints of file classification. In this paper, we propose a file classification algorithm to automatically develop multidimensional classification tree using original folder structure of the file server. The algorithm iteratively extracts duplicated words from the original tree to construct a set of independent trees. However, there is a case that the classification result is inconsistent. We thus improve the algorithm by introducing high level manual operations for tree checking and editing to solve the inconsistency problem.

英語フィールド

Author:
Tetsuro Kakeshita, Shota Yamaguchi
Title:
Iterative File Classification Algorithm using Multidimensional Tree
Announcement information:
International Conference on Convergence Content (ICCC 2012), Saga, Japan, December 14-17, 2012.
Keyword:
File classification, multidimensional tree, OLAP (online analytical processing), file server
An abstract:
We have proposed the notion of multidimensional classification to systematically arrange digital files. The method utilizes multidimensional tree each of which represents independent viewpoints of file classification. In this paper, we propose a file classification algorithm to automatically develop multidimensional classification tree using original folder structure of the file server. The algorithm iteratively extracts duplicated words from the original tree to construct a set of independent trees. However, there is a case that the classification result is inconsistent. We thus improve the algorithm by introducing high level manual operations for tree checking and editing to solve the inconsistency problem.


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