A New Clustering Algorithm Based Upon Flocking On Complex Network

TL;DR

This paper introduces a novel clustering algorithm inspired by flocking behavior on complex networks, utilizing long-range links and vector fields to efficiently group data points based on their class membership.

Contribution

It develops two clustering algorithms based on flocking dynamics on complex networks, incorporating long-range links to improve clustering speed and accuracy.

Findings

Data points in the same class converge to the same position.

The algorithms achieve fast convergence rates.

Experimental results show effective and efficient clustering.

Abstract

We have proposed a model based upon flocking on a complex network, and then developed two clustering algorithms on the basis of it. In the algorithms, firstly a \textit{k}-nearest neighbor (knn) graph as a weighted and directed graph is produced among all data points in a dataset each of which is regarded as an agent who can move in space, and then a time-varying complex network is created by adding long-range links for each data point. Furthermore, each data point is not only acted by its \textit{k} nearest neighbors but also \textit{r} long-range neighbors through fields established in space by them together, so it will take a step along the direction of the vector sum of all fields. It is more important that these long-range links provides some hidden information for each data point when it moves and at the same time accelerate its speed converging to a center. As they move in space according to the proposed model, data points that belong to the same class are located at a same position gradually, whereas those that belong to different classes are away from one another. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the rates of convergence of clustering algorithms are fast enough. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.