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Comparison of Clustering with Self Organizing Map and Fuzzy C-Means Algorithm

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dc.contributor.author Maung, Hsu Mon
dc.contributor.author Win, Tha Pyay
dc.date.accessioned 2019-08-06T02:01:25Z
dc.date.available 2019-08-06T02:01:25Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1833
dc.description.abstract Clustering partitions a set of objects into non-overlapping subsets called clusters such that objects inside each cluster are similar to each other and objects from different clusters are not similar. The set of non-overlapping clusters is called a partition. Neural networks are believed to possess some particularly valuable properties, since they are patterned after associative neural properties of the brain. Neural networks proceed by a process called learning. The Self-Organizing Map (SOM) is a stable neural network model for high-dimensional data analysis. Most classical clustering algorithms assign each data to exactly one cluster, thus forming a crisp partition of the given data, but fuzzy clustering allows for degrees of membership, to which data belongs to different clusters. The best known fuzzy clustering algorithm is fuzzy c-means (FCM) clustering algorithm which is straightforward generalization of classical crisp c-means algorithm. This system is implemented clustering multidimensional data by using SOM and FCM algorithms. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.title Comparison of Clustering with Self Organizing Map and Fuzzy C-Means Algorithm en_US
dc.type Article en_US

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