FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2560583217> ?p ?o ?g. }

• W2560583217 abstract "Over the past decade, the clustering ensemble has been emerged as a prominent method as far as the improving of clustering accuracy is concerned. Two major difficulties in clustering ensemble include diversity of clustering and consensus functions. Genetic algorithms are well known methods with high ability to resolve optimization problems including clustering. So far, limited genetic-based clustering ensemble algorithms have been developed. However, their clustering accuracy and convergence to group unlabeled samples are not still satisfied. Generally, associated common problems in traditional genetic algorithms include lose population diversity, clustering invalidity, and context insensitivity. In order to address the above mentioned challenges, this study is devoted towards the development of a clusterer and a clustering ensemble learning method based on incremental genetic algorithms addressing group unlabeled samples. Firstly, an architecture for the clustering ensemble based on incremental genetic-based algorithms is proposed consisting of two phases: (i) to produce cluster partitions as initial populations, (ii) to combine cluster partitions and to generate final clustering solution by incremental genetic based clustering ensemble learning algorithm. In the first and second phases, a threshold fuzzy c-means clustering algorithm as a clusterer and a pattern ensemble learning method based on the incremental genetic-based algorithms are proposed respectively. In the first phase, the quality of cluster partitions belonging to initial populations is measured, in terms of diversity and clustering accuracy. In the second phase, the performance of incremental genetic-based clustering ensemble algorithms is measured, in terms of clustering accuracy and convergence. A comprehensive experimental analysis is conducted by several experiments to evaluate the performance of the proposed clusterer and incremental genetic-based clustering ensemble algorithm which has been tested on the twelve benchmark datasets. In comparison to different clusterers, experimental results show that the proposed clusterer is able to produce cluster partitions with various diversity and desirable clustering accuracy. Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. In addition, experiments prove that incremental genetic-based clustering ensemble algorithm speed up to converge into an optimal clustering solution, where pattern ensemble learning method and the cluster partitions produced by the threshold fuzzy c-means clustering algorithm are employed as recombination operator and initial population, respectively." @default.
• W2560583217 created "2016-12-16" @default.
• W2560583217 creator A5047323464 @default.
• W2560583217 date "2012-08-01" @default.
• W2560583217 modified "2023-09-27" @default.
• W2560583217 title "Clustering ensemble learning method based on incremental genetic algorithms" @default.
• W2560583217 hasPublicationYear "2012" @default.
• W2560583217 type Work @default.
• W2560583217 sameAs 2560583217 @default.
• W2560583217 citedByCount "0" @default.
• W2560583217 crossrefType "dissertation" @default.
• W2560583217 hasAuthorship W2560583217A5047323464 @default.
• W2560583217 hasConcept C104047586 @default.
• W2560583217 hasConcept C119857082 @default.
• W2560583217 hasConcept C124101348 @default.
• W2560583217 hasConcept C149872217 @default.
• W2560583217 hasConcept C153180895 @default.
• W2560583217 hasConcept C154945302 @default.
• W2560583217 hasConcept C17212007 @default.
• W2560583217 hasConcept C186767784 @default.
• W2560583217 hasConcept C193143536 @default.
• W2560583217 hasConcept C22648726 @default.
• W2560583217 hasConcept C27964816 @default.
• W2560583217 hasConcept C33704608 @default.
• W2560583217 hasConcept C41008148 @default.
• W2560583217 hasConcept C45942800 @default.
• W2560583217 hasConcept C73555534 @default.
• W2560583217 hasConcept C94641424 @default.
• W2560583217 hasConceptScore W2560583217C104047586 @default.
• W2560583217 hasConceptScore W2560583217C119857082 @default.
• W2560583217 hasConceptScore W2560583217C124101348 @default.
• W2560583217 hasConceptScore W2560583217C149872217 @default.
• W2560583217 hasConceptScore W2560583217C153180895 @default.
• W2560583217 hasConceptScore W2560583217C154945302 @default.
• W2560583217 hasConceptScore W2560583217C17212007 @default.
• W2560583217 hasConceptScore W2560583217C186767784 @default.
• W2560583217 hasConceptScore W2560583217C193143536 @default.
• W2560583217 hasConceptScore W2560583217C22648726 @default.
• W2560583217 hasConceptScore W2560583217C27964816 @default.
• W2560583217 hasConceptScore W2560583217C33704608 @default.
• W2560583217 hasConceptScore W2560583217C41008148 @default.
• W2560583217 hasConceptScore W2560583217C45942800 @default.
• W2560583217 hasConceptScore W2560583217C73555534 @default.
• W2560583217 hasConceptScore W2560583217C94641424 @default.
• W2560583217 hasLocation W25605832171 @default.
• W2560583217 hasOpenAccess W2560583217 @default.
• W2560583217 hasPrimaryLocation W25605832171 @default.
• W2560583217 hasRelatedWork W1604980173 @default.
• W2560583217 hasRelatedWork W1902036342 @default.
• W2560583217 hasRelatedWork W1969057237 @default.
• W2560583217 hasRelatedWork W2035379061 @default.
• W2560583217 hasRelatedWork W2060220440 @default.
• W2560583217 hasRelatedWork W2086963655 @default.
• W2560583217 hasRelatedWork W2338231020 @default.
• W2560583217 hasRelatedWork W2369843047 @default.
• W2560583217 hasRelatedWork W2375439838 @default.
• W2560583217 hasRelatedWork W2382069867 @default.
• W2560583217 hasRelatedWork W2533880043 @default.
• W2560583217 hasRelatedWork W2731043560 @default.
• W2560583217 hasRelatedWork W2909248382 @default.
• W2560583217 hasRelatedWork W2943892938 @default.
• W2560583217 hasRelatedWork W2948004663 @default.
• W2560583217 hasRelatedWork W2973546633 @default.
• W2560583217 hasRelatedWork W3081416485 @default.
• W2560583217 hasRelatedWork W3155110726 @default.
• W2560583217 hasRelatedWork W3198541611 @default.
• W2560583217 hasRelatedWork W2515532987 @default.
• W2560583217 isParatext "false" @default.
• W2560583217 isRetracted "false" @default.
• W2560583217 magId "2560583217" @default.
• W2560583217 workType "dissertation" @default.