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Effective and Efficient Clustering Methods for Correlated Probabilistic Graphs

机译:相关概率图的有效聚类方法

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Recently, probabilistic graphs have attracted significant interests of the data mining community. It is observed that correlations may exist among adjacent edges in various probabilistic graphs. As one of the basic mining techniques, graph clustering is widely used in exploratory data analysis, such as data compression, information retrieval, image segmentation, etc. Graph clustering aims to divide data into clusters according to their similarities, and a number of algorithms have been proposed for clustering graphs, such as the pKwikCluster algorithm, spectral clustering, k-path clustering, etc. However, little research has been performed to develop efficient clustering algorithms for probabilistic graphs. Particularly, it becomes more challenging to efficiently cluster probabilistic graphs when correlations are considered. In this paper, we define the problem of clustering correlated probabilistic graphs. To solve the challenging problem, we propose two algorithms, namely the $PEEDR$ and the $CPGS$ clustering algorithm. For each of the proposed algorithms, we develop several pruning techniques to further improve their efficiency. We evaluate the effectiveness and efficiency of our algorithms and pruning methods through comprehensive experiments.
机译:最近,概率图吸引了数据挖掘社区的极大兴趣。可以看出,在各种概率图中,相邻边之间可能存在相关性。作为一种基本的挖掘技术,图聚类被广泛用于探索性数据分析中,例如数据压缩,信息检索,图像分割等。图聚类旨在根据数据的相似性将其划分为多个聚类,并且许多算法具有提出了用于聚类图的方法,例如pKwikCluster算法,谱聚类,k路径聚类等。但是,很少有研究来开发用于概率图的有效聚类算法。特别地,当考虑相关性时,有效地聚类概率图变得更具挑战性。在本文中,我们定义了聚类相关概率图的问题。为了解决这一难题,我们提出了两种算法,即$ PEEDR $和$ CPGS $聚类算法。对于每种提出的算法,我们开发了几种修剪技术以进一步提高其效率。我们通过综合实验评估算法和修剪方法的有效性和效率。

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