Wittgenstein's Family Resemblance Clustering Algorithm

TL;DR

This paper introduces Wittgenstein's Family Resemblance clustering, a graph-based machine learning method inspired by philosophical ideas, which effectively identifies clusters without prior shape or number assumptions.

Contribution

It develops a novel clustering algorithm based on philosophical concepts, utilizing a graph approach to detect overlapping similarities among data points.

Findings

Effective nonlinear clustering demonstrated on benchmark datasets

Does not require prior knowledge of number or shape of clusters

Outperforms traditional clustering methods in flexibility

Abstract

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.