TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization
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| Format: | Preprint |
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2024
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| _version_ | 1866918281930080256 |
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| author | Ma, Haowen Long, Zhiguo Meng, Hua |
| author_facet | Ma, Haowen Long, Zhiguo Meng, Hua |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_10084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization Ma, Haowen Long, Zhiguo Meng, Hua Machine Learning 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. |
| title | TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.10084 |