On an Improvement to Initial Value Dependency Problem of Two Fuzzy Clustering Algorithms for Categorical Multivariate Data
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- Suzuki Kazune
- Shibaura Institute of Technology
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- Kanzawa Yuchi
- Shibaura Institute of Technology
Bibliographic Information
- Other Title
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- カテゴリカル多変量データのための 2 種類のファジィクラスタリングに対する初期値依存性改善について
- カテゴリカル タヘンリョウ データ ノ タメ ノ 2シュルイ ノ ファジィクラスタリング ニ タイスル ショキチ イソンセイ カイゼン ニ ツイテ
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Abstract
<p>The results from fuzzy clustering algorithms are heavily affected from initial values, and an easy initial value setting produces local optimal solution or saddle point, resulting in poor clustering accuracy. In this report, an initial value setting is proposed using eigen pairs of Hessian for the objective function. Then, this setting is applied to the entropy-regularized and q-divergence-based fuzzy clustering algorithms for categorical multivariate data.</p>
Journal
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- Proceedings of the Fuzzy System Symposium
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Proceedings of the Fuzzy System Symposium 37 (0), 561-564, 2021
Japan Society for Fuzzy Theory and Intelligent Informatics
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Details 詳細情報について
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- CRID
- 1390853752277472256
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- NII Article ID
- 130008143684
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- NII Book ID
- AA12165648
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- ISSN
- 18820212
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- NDL BIB ID
- 031714007
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed