Abstract Feature selection is an essential technique to reduce the dimensionality problem in data mining task. Traditional feature selection algorithms are fail to scale on large space. This paper proposes a new method to solve dimensionality problem where clustering is integrating with correlation measure to produce good feature subset. First Irrelevant features are eliminated by using k-means clustering method and then non-redundant features are selected by correlation measure from each cluster. The proposed method is evaluate on Microarray and Text datasets and the results are compared with other renowned feature selection methods using Naive Bayes classifier. To verify the accuracy of the proposed method with different number of relevant features, percentagewise criteria is used. The experimental results reveal the efficiency and accuracy of the proposed method.

中文翻译：

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基于相关性的高维数据聚类特征选择

摘要 特征选择是数据挖掘任务中降维问题的重要技术。传统的特征选择算法无法在大空间上扩展。本文提出了一种解决维度问题的新方法，其中聚类与相关性度量相结合以产生良好的特征子集。首先使用k-means聚类方法去除不相关的特征，然后通过相关性度量从每个簇中选择非冗余特征。所提出的方法在微阵列和文本数据集上进行评估，并将结果与​​使用朴素贝叶斯分类器的其他著名特征选择方法进行比较。为了验证具有不同数量相关特征的所提出方法的准确性，使用百分比标准。