Researchers

Research Activities

Research Areas

• Informatics, Sensitivity (kansei) informatics
• Informatics, Soft computing
• Informatics, Intelligent informatics
• Informatics, Information theory

Research Interests

Data Science, Machine Learning, Soft Computing, Clustering

Published Papers

1. A Novel Noise Clustering Based on Local Outlier Factor
   Yukihiro Hamasuna; Yoshitomo Mori
   Lecture Notes in Computer Science  14376  , 179-191, 25, Oct. 2023  , Refereed
2. The relationship between Gaussian process based c-regression models and kernel c-regression models
   Yukihiro Hamasuna; Yuya Yokoyama; Kaito Takegawa
   2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS)  29, Nov. 2022  , Refereed
3. Network Clustering with Controlled Node Size
   Yukihiro Hamasuna; Shusuke Nakano; Yasunori Endo
   Modeling Decisions for Artificial Intelligence, LNAI 12898  , 243-256, Sep. 2021  , Refereed

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MISC

1. On Sequential Cluster Extraction Using Possibilistic Size Controll Clustering , Ryota Uto; Yukihiro Hamasuna , 第39回ファジィシステムシンポジウム 講演論文集 , Sep. 2023
   Summary:Clustering methods such as k-means and fuzzy c-means methods require the number of clusters to be determined in advance. In addition, conventional clustering methods may not be able to properly classify unbalanced data or data containing outliers. In this study, we propose a sequential cluster extraction method based on controlled-sized sequential possibilisitic clustering for imbalanced data and data with outliers. Furthermore, numerical experiments were conducted to confirm the effectiveness of the proposed method.
2. A Study on Cluster Validity Measures Based on Fuzzy Membership for Time-Series Data , ⃝Kenshin Fujita; Yukihiro Hamasuna , 第39回ファジィシステムシンポジウム 講演論文集 , Sep. 2023
   Summary:It is difficult to understand the structure of the high-dimensional data. Time-series data is an example of such high-dimensional data. Time-series data requires consideration of differences in period and number of sequences. The choice of dissimilarity and the clustering algorithm also affect the generated cluster structure. In particular, it is difficult to understand the cluster structure of time-series data because of its high dimensionality. The cluster validity measures are useful for evaluating the cluster partition of time-series data. In this study, we propose a cluster validity measure based on fuzzy membership for time-series data. It is shown that the proposed method performs as well as or better than existing methods through numerical experiments. It is also shown that the proposed method is useful for data that is close to the cluster center to which each data belongs and far from other cluster centers.
3. A Study on Parameter Estimation in Gaussian Process based c-Regression Models , ⃝Yuya Yokoyama; Yukihiro Hamasuna , 第39回ファジィシステムシンポジウム 講演論文集 , Sep. 2023
   Summary:Gaussian process based c-regression models (GPCRM) is a method to obtain the cluster partition and nonlinear regression models simultaneously. Since the regression model depends on the kernel parameters, it is difficult to obtain a regression model that captures the data structure depending on the kernel parameters. In this paper, we propose maximum marginal likelihood GPCRM(MML-GPCRM) as a method introducing kernel parameter estimation. The experimental results suggest that the MML- GPCRM estimated a better-fitting regression models than the GPCRM.

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Awards & Honors

1. Sep. 2012, 日本知能情報ファジィ学会, 奨励賞

Research Grants & Projects

1. 日本学術振興会, 科学研究費補助金 基盤研究（C), 構造的ゆらぎを伴うネットワークデータに対するクラスタリング手法の拡張と高度化
2. 公益財団法人電気通信普及財団, 研究調査助成, データ構造に対して頑健なクラスタリングの開発
3. Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists (B), 構造的ゆらぎを伴うグラフデータに対するクラスタリング手法の確立

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