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In this paper, we present a scalable evolutionary algorithm for clustering large and dynamic data sets, called Scalable Evolutionary Clustering with Self Adaptive Genetic Operators (Scalable ECSAGO). The proposed evolutionary clustering algorithm can adapt its genetic operators rate while the evolution leads to the optimal centers of the clusters. The sizes of the clusters are estimated using a hybrid...
While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field is dealing with high dimensional datasets. The class of clustering algorithms that utilises information from Principal Component Analysis has proven very successful in such datasets...
Cluster analysis is one of the several important tools in modern data analysis, and the clustering can be regarded as an optimization problem. The underlying assumption is that there are natural tendencies of cluster or group structure in the data and the goal is to be able to uncover this structure. In general, traditional clustering algorithms are suitable to implement clustering only if the feature...
A modified differential evolution (DE) algorithm is presented for clustering the pixels of an image in its intensity space. The algorithm requires no prior information about the number of naturally occurring clusters in the image. It employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to partition data that is...
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