Approximate spectral clustering using both reference vectors and topology of the network generated by growing neural gas

TL;DR

This paper introduces an approximate spectral clustering method that leverages growing neural gas to reduce computational costs and enhance clustering quality by utilizing network topology and reference vectors.

Contribution

It presents a novel spectral clustering approximation using GNG, improving efficiency and clustering performance over traditional spectral clustering methods.

Findings

Reduces computational time significantly.

Achieves equal or better clustering accuracy.

Utilizes network topology for improved data representation.

Abstract

Spectral clustering (SC) is one of the most popular clustering methods and often outperforms traditional clustering methods. SC uses the eigenvectors of a Laplacian matrix calculated from a similarity matrix of a dataset. SC has serious drawbacks: the significant increases in the time complexity derived from the computation of eigenvectors and the memory space complexity to store the similarity matrix. To address the issues, I develop a new approximate spectral clustering using the network generated by growing neural gas (GNG), called ASC with GNG in this study. ASC with GNG uses not only reference vectors for vector quantization but also the topology of the network for extraction of the topological relationship between data points in a dataset. ASC with GNG calculates the similarity matrix from both the reference vectors and the topology of the network generated by GNG. Using the network generated from a dataset by GNG, ASC with GNG achieves to reduce the computational and space complexities and improve clustering quality. In this study, I demonstrate that ASC with GNG effectively reduces the computational time. Moreover, this study shows that ASC with GNG provides equal to or better clustering performance than SC.