Self-Updating Process with B-splines on Functional Data Clustering
碩士 === 國立臺北大學 === 統計學系 === 106 === The self-updating process (SUP) is competitive in clustering data with noise, data with a large number of clusters, and unbalanced data. This paper presents an extension of SUP to functional data clustering by the use of B-spline basis functions. The curves in data...
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| Format: | Others |
| Language: | zh-TW |
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2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/23uyn2 |
| Summary: | 碩士 === 國立臺北大學 === 統計學系 === 106 === The self-updating process (SUP) is competitive in clustering data with noise, data with a large number of clusters, and unbalanced data. This paper presents an extension of SUP to functional data clustering by the use of B-spline basis functions. The curves in data are first represented by B-spline functions, then the updating process is to perform clustering in the B-spline space. This paper provides comparison results between the proposed extension of SUP and other existing methods for functional data clustering.
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