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dc.contributor.advisorKeller, James M.eng
dc.contributor.authorWu, Wenlongeng
dc.date.issued2018eng
dc.date.submitted2018 Springeng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The possibilistic c-means (PCM) was developed as an extension of the fuzzy c-means (FCM) clustering algorithm by abandoning the membership sum-to-one constraint. In the PCM, each cluster is independent of the other clusters and can be processed separately. Because of this separability, the sequential possibilistic one-mean (SP1M) was proposed to find clusters sequentially by running the possibilistic one-mean (P1M) c times. One critical problem in both PCM and SP1M is how to determine the parameter [subscript]. The sequential possibilistic one-mean with adaptive eta (SP1M-AE) was developed to allow [subscript] to change during iterations. In this thesis, a new dynamic adaptation mechanism for the parameter [subscript] in each cluster is inserted into SP1M. The resultant algorithm, called the sequential possibilistic one-mean with dynamic eta (SP1M-DE) is shown to provide superior performance over PCM, SP1M, and SP1M-AE in determining correct clustering results. In this thesis, the family of the SP1M clustering algorithm is also extended to combine local spatial information in an image. The resultant algorithm, called sequential possibilistic local information one-mean (SPLI1M) is shown to have a better performance on image segmentation over the fuzzy local information c-means (FLICM), the possibilistic local information c-means (PLICM), and the family of SP1M without combing local spatial information in image.eng
dc.format.extentvii, 46 pages : illustrationeng
dc.identifier.urihttps://hdl.handle.net/10355/66216
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the campuses of the University of Missouri.eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.titleThe family of sequential possibilistic one-mean clustering algorithmseng
dc.typeThesiseng
thesis.degree.disciplineComputer engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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