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首页> 外文期刊>International journal of machine learning and cybernetics >Soft subspace clustering with an improved feature weight self-adjustment mechanism
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Soft subspace clustering with an improved feature weight self-adjustment mechanism

机译：具有改进的特征权重自我调整机制的软子空间聚类

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摘要

Traditional clustering algorithms are often defeated by high dimensionality. In order to find clusters hiding in different subspaces, soft subspace clustering has become an effective means of dealing with high dimensional data. However, most existing soft subspace clustering algorithms contain parameters which are difficult to be determined by users in real-world applications. A new soft subspace clustering algorithm named SC-IFWSA is proposed, which uses an improved feature weight self-adjustment mechanism IFWSA to update adaptively the weights of all features for each cluster according to the importance of the features to clustering quality and does not require users to set any parameter values. In addition, SC-IFWSA can overcome the traditional FWSA mechanism which may fail to calculate feature weights in some particular cases. In comparison with its related approaches, the experimental results carried out on ten data sets demonstrate the effectiveness and feasibility of the proposed method.
机译：传统的聚类算法通常被高维所击败。为了找到隐藏在不同子空间中的聚类，软子空间聚类已成为处理高维数据的有效手段。但是，大多数现有的软子空间聚类算法包含的参数在实际应用中很难被用户确定。提出了一种新的名为SC-IFWSA的软子空间聚类算法，该算法使用改进的特征权重自我调整机制IFWSA根据特征对聚类质量的重要性自适应地更新每个聚类的所有特征的权重，并且不需要用户设置任何参数值。此外，SC-IFWSA可以克服传统的FWSA机制，该机制在某些特定情况下可能无法计算特征权重。与相关方法相比，在十个数据集上进行的实验结果证明了该方法的有效性和可行性。

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  • 作者

    Gongde Guo; Si Chen; Lifei Chen;

  • 作者单位

    School of Mathematics and Computer Science,Fujian Normal University, Fuzhou, China,Key Laboratory of Network Security and Cryptography,Fujian Normal University, Fuzhou, China;

    School of Mathematics and Computer Science,Fujian Normal University, Fuzhou, China,Key Laboratory of Network Security and Cryptography,Fujian Normal University, Fuzhou, China;

    School of Mathematics and Computer Science,Fujian Normal University, Fuzhou, China,Key Laboratory of Network Security and Cryptography,Fujian Normal University, Fuzhou, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    data mining; clustering; subspace; high dimensional data; feature weighting;

    机译：数据挖掘;集群子空间高维数据;特征权重;

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