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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Robust Ensemble Clustering Using Probability Trajectories
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Robust Ensemble Clustering Using Probability Trajectories

机译：使用概率轨迹的鲁棒集成聚类

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摘要

Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorporate global information to refine the local links. To address these two limitations, in this paper, we propose a novel ensemble clustering approach based on sparse graph representation and probability trajectory analysis. In particular, we present the elite neighbor selection strategy to identify the uncertain links by locally adaptive thresholds and build a sparse graph with a small number of probably reliable links. We argue that a small number of probably reliable links can lead to significantly better consensus results than using all graph links regardless of their reliability. The random walk process driven by a new transition probability matrix is utilized to explore the global information in the graph. We derive a novel and dense similarity measure from the sparse graph by analyzing the probability trajectories of the random walkers, based on which two consensus functions are further proposed. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.
机译：尽管近年来已经开发了许多成功的集成聚类方法，但是对于大多数现有方法仍然存在两个局限性。首先，他们大多忽略了不确定链接的问题，这可能会误导整个共识过程。其次，他们通常缺乏整合全球信息以完善本地链接的能力。为了解决这两个局限性，本文提出了一种基于稀疏图表示和概率轨迹分析的集成聚类方法。特别是，我们提出了一种精英邻居选择策略，以通过局部自适应阈值识别不确定的链接，并建立具有少量可能可靠链接的稀疏图。我们认为，不论可靠性如何，与使用所有图形链接相比，少数可能可靠的链接可以导致更好的共识结果。由新的转移概率矩阵驱动的随机游走过程被用来探索图中的全局信息。通过分析随机步行者的概率轨迹，从稀疏图推导出一种新颖且密集的相似性度量，并在此基础上进一步提出了两个共识函数。在多个真实世界数据集上的实验结果证明了我们方法的有效性和效率。

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