Graph partitioning advance clustering technique

TL;DR

This paper introduces an advanced graph partitioning method that enhances clustering by utilizing eigenvector analysis of lattice graphs combined with K-means, improving pattern detection in large datasets.

Contribution

It proposes a novel approach integrating Fiedler's eigenvector method with K-means for more effective clustering of lattice graphs.

Findings

Improved clustering accuracy on large datasets

Effective partitioning of m-dimensional lattice graphs

Enhanced pattern recognition capabilities

Abstract

Clustering is a common technique for statistical data analysis, Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. Often, distance measures are used. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. This paper also considers the partitioning of m-dimensional lattice graphs using Fiedler's approach, which requires the determination of the eigenvector belonging to the second smallest Eigenvalue of the Laplacian with K-means partitioning algorithm.