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A Weighted Genetic Algorithm Based Method for Clustering of Heteroscaled Datasets

机译:基于加权遗传算法的多尺度数据集聚类方法

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This paper introduces a weighted genetic algorithm (GA) based clustering method for datasets with differently scaled dimensions. Several types of synthetic two dimensional scatter data were clustered using the typical k-means clustering method. The weighted GA-based clustering method was developed to address the problem of clustering data with differently scaled (heteroscaled) dimensions. Cluster analysis results obtained from using this method was compared to the results produced from the application of the traditional k-means clustering. By introducing weights in the fitness evaluation component of the meta-heuristic search method, a more efficient clustering of heteroscaled data was produced. In real applications, this method can be used in cluster analyses of scatter data with significantly different scales in dimensions, such as kurtosis versus fatigue damage relationship scatter data.
机译:本文介绍了一种基于加权遗传算法(GA)的聚类方法,用于尺度不同的数据集。使用典型的k均值聚类方法对几种类型的合成二维散点数据进行聚类。开发了基于加权GA的聚类方法,以解决具有不同比例(异比例)维的聚类数据的问题。将使用此方法获得的聚类分析结果与应用传统k均值聚类得到的结果进行比较。通过在元启发式搜索方法的适应性评估组件中引入权重,可以生成更有效的异标度数据聚类。在实际应用中,此方法可用于散度数据的聚类分析,这些散度数据的尺度明显不同,例如峰度与疲劳损伤关系散度数据。

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