# Novel Framework for Spectral Clustering using Topological Node Features(TNF)

## TL;DR

This paper introduces a new spectral clustering framework that leverages Topological Node Features to improve the construction of affinity matrices, enhancing clustering performance on various datasets.

## Contribution

The paper proposes a novel framework utilizing Topological Node Features for better affinity matrix construction in spectral clustering, addressing limitations of existing methods.

## Key findings

- Outperforms standard spectral clustering on synthetic, UCI, and MNIST datasets.
- Effectively captures local density and structural features for improved clustering.
- Demonstrates robustness across diverse data types and structures.

## Abstract

Spectral clustering has gained importance in recent years due to its ability to cluster complex data as it requires only pairwise similarity among data points with its ease of implementation. The central point in spectral clustering is the process of capturing pair-wise similarity. In the literature, many research techniques have been proposed for effective construction of affinity matrix with suitable pair- wise similarity. In this paper a general framework for capturing pairwise affinity using local features such as density, proximity and structural similarity is been proposed. Topological Node Features are exploited to define the notion of density and local structure. These local features are incorporated into the construction of the affinity matrix. Experimental results, on widely used datasets such as synthetic shape datasets, UCI real datasets and MNIST handwritten datasets show that the proposed framework outperforms standard spectral clustering methods.