用哲学思想设计新聚类算法，无需预设簇数和形状。
Wittgenstein's Family Resemblance Clustering Algorithm
- 基于维特根斯坦的家族相似性思想，用重叠特征连接数据点
- 在多个基准数据集上表现良好，能发现非线性结构簇
- 适合处理形状不规则、数量未知的聚类任务
本文提出一种新型哲学启发式聚类方法——维特根斯坦家族相似性（WFR）算法及其核版本。该方法源自维特根斯坦《哲学研究》（1953）中的家族相似性概念：一个类别成员通过相互重叠的相似性联系，而非单一定义属性。基于此，我们构建图模型，计算邻近数据点间的相似性得分，经阈值处理后形成相似性图，连通分量即为聚类结果。实验表明，WFR是一种有效的非线性聚类算法，无需事先知道簇的数量或假设簇的形状，在多个基准数据集上表现优异。
原文摘要 · Abstract (English)
This paper, introducing a novel method in philomatics, draws on Wittgenstein's concept of family resemblance from analytic philosophy to develop a clustering algorithm for machine learning. According to Wittgenstein's Philosophical Investigations (1953), family resemblance holds that members of a concept or category are connected by overlapping similarities rather than a single defining property. Consequently, a family of entities forms a chain of items sharing overlapping traits. This philosophical idea naturally lends itself to a graph-based approach in machine learning. Accordingly, we propose the Wittgenstein's Family Resemblance (WFR) clustering algorithm and its kernel variant, kernel WFR. This algorithm computes resemblance scores between neighboring data instances, and after thresholding these scores, a resemblance graph is constructed. The connected components of this graph define the resulting clusters. Simulations on benchmark datasets demonstrate that WFR is an effective nonlinear clustering algorithm that does not require prior knowledge of the number of clusters or assumptions about their shapes.
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