# Wittgenstein's Family Resemblance Clustering Algorithm

arXiv: https://arxiv.org/abs/2601.01127 · HTML: https://mlsanity.com/paper/2601.01127/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces](https://mlsanity.com/paper/1511.00628/) (2015)
- [Fitting A Mixture Distribution to Data: Tutorial](https://mlsanity.com/paper/1901.06708/) (2019)
- [On Philomatics and Psychomatics for Combining Philosophy and Psychology with Mathematics](https://mlsanity.com/paper/2308.13738/) (2023)

## Built on by (most cited first, 1 of 1)

- [Data Evolution by Wittgenstein's Rule Following](https://mlsanity.com/paper/2606.22674/) (2026)

## Abstract (arXiv)

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.