Please use this identifier to cite or link to this item:
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.
DOI: 10.32657/10356/47494
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

Files in This Item:
File Description SizeFormat 
SCE_THESES_5.pdf11.28 MBAdobe PDFThumbnail

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.