A Flocking Based Algorithm for Document Clustering Analysis

Department

Computer Science

Document Type

Article

Publication Title

Journal of Systems Architecture

ISSN

1318-7621

Volume

52

Issue

8--9

DOI

10.1016/j.sysarc.2006.02.003

First Page

505

Last Page

515

Publication Date

8-1-2006

Abstract

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K-means and the Ant clustering algorithm for real document clustering.

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