A Novel Clustering Algorithm Based on a Modified Model of Random Walk

TL;DR

This paper presents two innovative clustering algorithms based on a modified random walk model, where data points move and interact dynamically, leading to automatic formation of clusters with demonstrated efficiency and effectiveness.

Contribution

The paper introduces a new modified random walk model and develops two clustering algorithms that adaptively form clusters through data point interactions and feedback mechanisms.

Findings

Data points cluster reasonably and efficiently in tests.

The algorithms outperform some existing methods.

Clusters form automatically through the proposed process.

Abstract

We introduce a modified model of random walk, and then develop two novel clustering algorithms based on it. In the algorithms, each data point in a dataset is considered as a particle which can move at random in space according to the preset rules in the modified model. Further, this data point may be also viewed as a local control subsystem, in which the controller adjusts its transition probability vector in terms of the feedbacks of all data points, and then its transition direction is identified by an event-generating function. Finally, the positions of all data points are updated. As they move in space, data points collect gradually and some separating parts emerge among them automatically. As a consequence, data points that belong to the same class are located at a same position, whereas those that belong to different classes are away from one another. Moreover, the experimental results have demonstrated that data points in the test datasets are clustered reasonably and efficiently, and the comparison with other algorithms also provides an indication of the effectiveness of the proposed algorithms.