Evolutionary computing for unsupervised clustering methods
101 p.
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2011
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sg-ntu-dr.10356-474942023-03-04T00:43:07Z Evolutionary computing for unsupervised clustering methods Do, Anh Duc Cho Siu-Yeung, David School of Computer Engineering DRNTU::Engineering::Computer science and engineering 101 p. Clustering represents a core research area of machine learning. It has been widely used in data processing and system learning where characteristics of the feature vectors, such as "localization" are defined or learned. Clustering algorithm attempts to organize unlabeled feature vectors into clusters such that within the same group, feature vectors are considered to be more similar than others of different groups. Among available clustering methods, Hard C-means (HCM) clustering represents non-overlapping clustering category while Fuzzy C-means clustering (FCM) represents the overlapping category. FCM enhances HCM with the introduction of fuzzy concept which is deemed closer to human cognition system. MASTER OF ENGINEERING (SCE) 2011-12-27T08:26:57Z 2011-12-27T08:26:57Z 2009 2009 Thesis Do, A. D. (2009). Evolutionary computing for unsupervised clustering methods. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/47494 10.32657/10356/47494 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Computer science and engineering Do, Anh Duc Evolutionary computing for unsupervised clustering methods |
| description |
101 p. |
| author2 |
Cho Siu-Yeung, David |
| author_facet |
Cho Siu-Yeung, David Do, Anh Duc |
| format |
Theses and Dissertations |
| author |
Do, Anh Duc |
| author_sort |
Do, Anh Duc |
| title |
Evolutionary computing for unsupervised clustering methods |
| title_short |
Evolutionary computing for unsupervised clustering methods |
| title_full |
Evolutionary computing for unsupervised clustering methods |
| title_fullStr |
Evolutionary computing for unsupervised clustering methods |
| title_full_unstemmed |
Evolutionary computing for unsupervised clustering methods |
| title_sort |
evolutionary computing for unsupervised clustering methods |
| publishDate |
2011 |
| url |
https://hdl.handle.net/10356/47494 |
| _version_ |
1759858250095788032 |