Element-Level Clustering of Feature Vectors Considering Correlations for Analyzing Image Data
Element-Level Clustering of Feature Vectors Considering Correlations for Analyzing Image Data
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DOI：
10.1109/skg.2016.031
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
M. Omachi;S. Omachi
中科院分区：
文献类型：
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作者：
M. Omachi;S. Omachi
聚类是数据分析的基本工具。通常，数据的所有属性都用于聚类。然而，如果一组属性可以被划分为有意义的子集，则对每个子集的数据进行聚类可能是有效的。在本文中，我们提出了一种方法，用于将特征向量的元素集划分为有意义的子集。考虑到元素之间的依赖性，相关性被用作聚类的度量。为了有效地解决优化问题，使用了图切割技术。在将元素集合划分为子集之后，对每个子集执行聚类。在手写体图像库上的实验表明了该方法的有效性。
Clustering is a fundamental tool for data analysis. Typically, all attributes of the data are used for clustering. However, if a set of attributes can be divided into meaningful subsets, it may be effective to cluster the data for each subset. In this paper, we propose a method for dividing the set of elements of feature vectors into meaningful subsets. Considering the dependencies between the elements, the correlation is used as the metric for clustering. In order to effectively solve the optimization problem, a technique for graph cut is used. After dividing the set of elements into subsets, clustering is performed for each subset. Experiments using a handwritten image database show the effectiveness of the proposed method.