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Improved Cuckoo Search Clustering Algorithm (ICSCA)

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dc.contributor.author Zaw, Moe Moe
dc.contributor.author Mon, Ei Ei
dc.date.accessioned 2019-07-12T05:47:50Z
dc.date.available 2019-07-12T05:47:50Z
dc.date.issued 2013-02-26
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/867
dc.description.abstract Clustering is a division of data into groups of similar objects. Each group called a cluster consists of objects that are similar between themselves and dissimilar compared to objects of the other groups. Cuckoo Search Clustering Algorithm (CSCA) is a recently developed nature inspired, unsupervised classification method, based on the most recent meta-heuristic algorithm, stirred by the breeding strategy of the parasitic bird, the cuckoo. To better exploit the search space and to enhance the accuracy of this algorithm, an Improved Cuckoo Search Clustering Algorithm (ICSCA) is proposed in this paper. Normally, in the search space, a substantial fraction of the new solutions should be generated by far field randomization and whose locations should be far enough from the current best solution, this will make sure the system will not be trapped in a local optimum. This ICSCA algorithm that is expected to find the global cuckoo solution and exploit the search space more thoroughly than CSCA algorithm is proposed. en_US
dc.language.iso en en_US
dc.publisher Eleventh International Conference On Computer Applications (ICCA 2013) en_US
dc.subject Data Clusering en_US
dc.subject Cuckoo Search en_US
dc.subject Cuckoo Search Clustering Algorithm en_US
dc.subject Improved Cuckoo Search Clustering Algorithm en_US
dc.title Improved Cuckoo Search Clustering Algorithm (ICSCA) en_US
dc.type Article en_US

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