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- W2897631922 abstract "Clustering has become an important mechanism for exploratory data analysis and understanding. Many Evolutionary Algorithms (EAs) have been applied for data clustering, given their flexibility to deal with difficult environments and their capabilities to escape from local minima points from the problem search space. However, given their stochastic nature, EAs may be slower to converge in comparison to traditional clustering models, such as K-Means algorithm, if no historical information is provided. In this work, Hybrid Intelligent Systems (HISs) based on K-Means and Group Search Optimization (GSO) technique are presented, in such a way that K-Means is used as a local search operator in an attempt to improve GSO initial population. Four different population initialization schemes are proposed. Experiments are performed on fifteen real benchmark data sets to evaluate the efficiency of proposed initialization schemes in comparison to standard K-Means and other well-known hybrid K-Means and EAs models from data clustering literature. Experimental results showed the influence of population initialization on the behavior of GSO while dealing with clustering task, and also, the relevance of contextual information to improve initial population for EAs." @default.
- W2897631922 created "2018-10-26" @default.
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- W2897631922 date "2018-07-01" @default.
- W2897631922 modified "2023-09-26" @default.
- W2897631922 title "Alternative Population Initialization Schemes for Group Search Optimization for Data Clustering" @default.
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- W2897631922 doi "https://doi.org/10.1109/cec.2018.8477752" @default.
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