An approach to the construction of event archipelago for chance discovery via genetic algorithm based on correlation matrix

Zhenya Zhang, Lin Zhang, Shuguan Zhang
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Abstract

Chance discovery is a new research topic on cognition psychology inspired deep data analysis. Scene is the contact point of human process and computer process in the double helical model of chance discovery. Event archipelago is the basic foundation for scene construction. Event archipelago and island are defined in this paper. Problem of event archipelago for Chance Discovery based on correlation matrix is a NP hard problem and to solve this problem instantly, GeneticCEA, an approximate algorithm based on genetic algorithm is presented. This paper discusses the performance of GeneticCEA too. Experimental results show that GeneticCEA can run with excellent performance for clustering task while it is treated as a kind of task for the construction of event archipelago in chance discovery.
基于相关矩阵的遗传算法构建事件群的机会发现方法
机会发现是受深度数据分析启发的认知心理学新研究课题。在偶然性发现的双螺旋模型中,场景是人类过程和计算机过程的接触点。事件群是场景构建的基础。本文定义了事件群岛和事件岛。基于相关矩阵的事件群偶然发现问题是一个NP困难问题,为了快速求解该问题,提出了一种基于遗传算法的近似算法GeneticCEA。本文还讨论了GeneticCEA的性能。实验结果表明,将GeneticCEA作为一种构建偶然发现事件群的任务,能够以优异的性能运行在聚类任务中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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