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Data Clustering and Validation For Traffic Management Support System

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dc.contributor.author Nawrin, Sadia
dc.date.accessioned 2016-11-07T08:44:12Z
dc.date.available 2016-11-07T08:44:12Z
dc.date.issued 8/10/2016
dc.identifier.uri http://dspace.ewubd.edu/handle/2525/1942
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. en_US
dc.description.abstract Clustering is used to classify or group the sets of data in unsupervised way. Different clustering algorithms including partition k-mean and hierarchical k mean with Elbow method, Davies boulder index, Dunn index, Silhouette coefficient are implemented to find out the optimal classification on the features of traffic management support system. Thereafter, the validity of the optimal classes are analyzed by WSS (within sum of square) errors. Dunn index seems better than others. Correlation method is used to find the better clustering algorithm between K-mean and hierarchical algorithms. According to correlation partition k-means performs better. Thus, partition k mean with Dunn index is used to determine the optimum number of cluster for five dimensional road weights. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;CSE00023
dc.subject Data Clustering en_US
dc.title Data Clustering and Validation For Traffic Management Support System en_US
dc.type Thesis en_US


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