Penalized K-Nearest-Neighbor-Graph Based Metrics for Clustering

TL;DR

This paper introduces the Penalized k-Nearest-Neighbor-Graph (PKNNG) metric, a novel distance measure designed for clustering data with complex manifold structures, demonstrating promising results on artificial and real datasets.

Contribution

The paper presents a new PKNNG metric that effectively handles manifold-structured data, compatible with various clustering algorithms, and validated through extensive experiments.

Findings

PKNNG improves clustering accuracy on manifold data

Effective in both artificial and real-world datasets

Shows promising results compared to traditional metrics

Abstract

A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space. In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with an exponentially penalized weight for connecting the sub-graphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs. We use three artificial datasets in four different embedding situations to evaluate the behavior of the new metric, including a comparison among different clustering methods. We also evaluate the new metric in a real world application, clustering the MNIST digits dataset. In all cases the PKNNG metric shows promising clustering results.