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Published in:
2011 | OriginalPaper | Chapter
On Hard c-Means Using Quadratic Penalty-Vector Regularization for Uncertain Data
Authors : Yasunori Endo, Arisa Taniguchi, Aoi Takahashi, Yukihiro Hamasuna
Published in: Modeling Decision for Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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Clustering is one of the unsupervised classification techniques of the data analysis. Data are transformed from a real space into a pattern space to apply clustering methods. However, the data cannot be often represented by a point because of uncertainty of the data, e.g., measurement error margin and missing values in data. In this paper, we introduce quadratic penalty-vector regularization to handle such uncertain data into hard
c
-means (HCM) which is one of the most typical clustering algorithms. First, we propose a new clustering algorithm called hard
c
-means using quadratic penalty-vector regularization for uncertain data (HCMP). Second, we propose sequential extraction hard
c
-means using quadratic penalty-vector regularization (SHCMP) to handle datasets whose cluster number is unknown. Moreover, we verify the effectiveness of our propose algorithms through some numerical examples.