Harmony search-based fuzzy clustering algorithms for image segmentation.

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dc.contributor.authorAlia, Osama Moh’d Radi
dc.date.accessioned2018-11-13T00:45:59Z
dc.date.available2018-11-13T00:45:59Z
dc.date.issued2011-02
dc.description.abstractAlgoritma-algoritma pengkelompokan kabur, yang tergolong di dalam kategori pembelajaran mesin tanpa selia, adalah di antara kaedah segmentasi imej yang paling berjaya. Namun demikian, terdapat dua isu utama yang membataskan keberkesanan kaedah ini: kepekaan terhadap pemilihan pusat kelompok permulaan dan ketidakpastian terhadap bilangan kelompok sebenar di dalam set data. Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given dataset.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/7072
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectClusteringen_US
dc.subjectSegmentationen_US
dc.titleHarmony search-based fuzzy clustering algorithms for image segmentation.en_US
dc.typeThesisen_US
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