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首页> 外文会议>2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare >Evolutionary clustering algorithm using criterion-knowledge-ranking for multi-objective optimization
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Evolutionary clustering algorithm using criterion-knowledge-ranking for multi-objective optimization

机译：基于准则知识排序的进化聚类算法用于多目标优化

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There are variety of methods available to solve multi-objective optimization problems, very few utilizes criterion linkage between data objects in the searching phase, to improve final result. This article proposes an evolutionary clustering algorithm for multi-objective optimization. This paper aims to identify more relevant features based on criterion knowledge from the given data sets and also adopts neighborhood learning to improve the diversity and efficacy of the algorithm. This research is an extension of the previous work named neighborhood learning using k-means genetic algorithm (FS-NLMOGA) which maximizes the compactness of the cluster and accuracy of the solution through constrained feature selection. The proposed objective finds the closest feature subset from the selected features of the data sets that also minimizes the cost while maintain the quality of the solution. The resultant cluster were analyzed and validated using cluster validity indexes. The proposed algorithm is tested with several UCI real-life data sets. The experimental results substantiates that the algorithm is efficient and robust.
机译：解决多目标优化问题的方法多种多样，在搜索阶段很少利用数据对象之间的准则链接来改善最终结果。本文提出了一种用于多目标优化的进化聚类算法。本文旨在基于给定数据集的准则知识来识别更多相关特征，并通过邻域学习来提高算法的多样性和有效性。这项研究是对以前使用k均值遗传算法（FS-NLMOGA）进行的邻域学习的扩展，该算法通过选择受限制的特征来最大程度地提高聚类的紧凑性和解决方案的准确性。拟议的目标从数据集的选定特征中找到最接近的特征子集，这也可以最大程度地降低成本，同时保持解决方案的质量。使用聚类有效性指标对所得聚类进行分析和验证。所提出的算法已通过多个UCI真实数据集进行了测试。实验结果证实了该算法的有效性和鲁棒性。

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