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Clustering streaming data has gained importance in recent years due to an expanding opportunity to discover knowledge in widely available data streams. As streams are potentially evolving and unbounded sequence of data objects, clustering algorithms capable of performing fast and incremental processing of data points are necessary. This paper presents a method of clustering high-dimensional data streams...
Boundary points detection of clusters is important in image processing, machine learning and so on. We propose a boundary detecting algorithm called BDDTS (Boundary Detection algorithm of clusters based on Dual Threshold Segmentation), which is based on the characteristics of the distribution of boundary points. The algorithm, which firstly accrues to the different cost function values of data points,...
Clustering categorical data faces two challenges, one is lacking of inherent similarity measure, and the other is that the clusters are prone to being embedded in different subspace. In this paper, we propose the first divisive hierarchical clustering algorithm for categorical data. The algorithm, which is based on multiple correspondence analysis (MCA), is systematic, efficient and effective. In...
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