TANGO: 基于典型性感知的非局部模式寻求与图割优化的聚类
TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization
AI总结：
TANGO通过引入典型性概念和改进的图割方法，实现非局部模式寻求与聚类优化，提升聚类效果。
摘要：
基于密度的模式寻求方法从低密度点向更高密度邻居生成一种密度递增依赖。当前的模式寻求方法通过打破某些依赖连接来识别模式，但依赖于局部数据特征，需要针对不同数据集进行案例设置或人工干预才能有效。为了解决这个问题，我们引入了一个新的概念，称为典型性，通过从全局视角探索局部定义的依赖，以量化一个点作为模式的置信度。我们设计了一种算法，利用全局视角的典型性来有效且高效地识别模式。为了实现和验证我们的想法，我们设计了一种名为TANGO的聚类方法，它不仅利用典型性来检测模式，还利用改进的路径相似度图割来将数据聚集到最终的聚类中。此外，本文还对所提出算法进行了某些理论分析。在几个合成和广泛现实数据集上的实验结果证明了TANGO的有效性和优越性。代码可在https://github.com/SWJTU-ML/TANGO_code获取。
英文摘要：
Density-based mode-seeking methods generate a \emph{density-ascending dependency} from low-density points towards higher-density neighbors. Current mode-seeking methods identify modes by breaking some dependency connections, but relying heavily on local data characteristics, requiring case-by-case threshold settings or human intervention to be effective for different datasets. To address this issue, we introduce a novel concept called \emph{typicality}, by exploring the \emph{locally defined} dependency from a \emph{global} perspective, to quantify how confident a point would be a mode. We devise an algorithm that effectively and efficiently identifies modes with the help of the global-view typicality. To implement and validate our idea, we design a clustering method called TANGO, which not only leverages typicality to detect modes, but also utilizes graph-cut with an improved \emph{path-based similarity} to aggregate data into the final clusters. Moreover, this paper also provides some theoretical analysis on the proposed algorithm. Experimental results on several synthetic and extensive real-world datasets demonstrate the effectiveness and superiority of TANGO. The code is available at https://github.com/SWJTU-ML/TANGO_code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
