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|Title:||Evolutionary computing for unsupervised clustering methods||Authors:||Do, Anh Duc||Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2009||Source:||Do, A. D. (2009). Evolutionary computing for unsupervised clustering methods. Master’s thesis, Nanyang Technological University, Singapore.||Abstract:||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.||Description:||101 p.||URI:||https://hdl.handle.net/10356/47494||DOI:||10.32657/10356/47494||Rights:||Nanyang Technological University||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Theses|
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