| Project/Area Number |
15540129
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | Kagoshima University |
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Principal Investigator |
INADA Koichi Kagoshima University, Faculty of Science, Professor, 理学部, 教授 (20018899)
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| Co-Investigator(Kenkyū-buntansha) |
YAMATO Hajime Kagoshima University, Faculty of Science, Professor, 理学部, 教授 (90041227)
KONDO Masao Kagoshima University, Faculty of Science, Professor, 理学部, 教授 (70117505)
YADOHISA Hiroshi Kagoshima University, Faculty of Science, Associate Professor, 理学部, 助教授 (50244223)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2004: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2003: ¥2,500,000 (Direct Cost: ¥2,500,000)
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| Keywords | Cluster analysis / Agglomerative hierarchical clustering / Monotonicity / Asymmetric dissimilarity data / 許容性 / 非対称凝縮型階層的分類法 / 階層的分類法 |
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| Research Abstract |
The agglomerative hierarchical clustering algorithms is a generic name of a series of talgorithms used best in the cluster analysis. It is easy to interpret because the agglomerative hierarchical clustering algorithms can classify the object by using a dissimilarity, and can show the formation situation of the clustering in the sight in the dendrogram and is used in a lot of fields. When the data is symmetric, we proposed the index that pays attention to "space distortion", "monotonicity of the combined distance", "Structured ratio for measuring the +clustering results", and the result of the cluster analysis has been evaluated multilaterally, The result is announced as Akinobu Takeuchi, Hiroshi Yadohisa and Koichi Inada "Evaluation of the classification result in the agglomerative hierarchical clustering algorithms" (the society by the project research of the Institute of Statistical Mathematics (2004)). Moreover, we investigated the crisp and fuzzy k-means clustering algorithms for multivariate functional data in association with this research.
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