Algorithm for Text Clustering Optimized with Particle Swarm Optimization

This paper focuses on the methodology of applying Particle Swarm Optimization (PSO) for text clustering tasks. PSO is a swarm intelligence-based optimization algorithm effective for solving diverse optimization problems. We present a MATLAB implementation of this algorithm specifically designed for text clustering applications.

First, we explain the working mechanism and fundamental concepts of PSO, including particle position updates and velocity calculations. Then, we detail the adaptation of PSO for text clustering, covering text data representation using TF-IDF vectors and defining appropriate objective functions to measure text similarity through cosine distance metrics. We describe PSO's iterative optimization process and provide guidelines for parameter selection, including swarm size, inertia weight, and acceleration coefficients.

Subsequently, we demonstrate the MATLAB implementation of the PSO text clustering algorithm. The code structure includes data preprocessing functions for text vectorization, main PSO optimization loops with position updating logic, and clustering evaluation modules. Key MATLAB functions such as psooptimset for parameter configuration and custom similarity calculation functions are explained with practical usage examples. We also cover text data preprocessing techniques and clustering quality assessment methods using metrics like silhouette coefficients and purity scores.

Finally, we validate the algorithm's effectiveness through experimental analysis using standard text datasets (e.g., Reuters-21578). Results include clustering visualization through dimensionality reduction techniques and quantitative comparisons with conventional methods like K-means and hierarchical clustering. We discuss PSO's advantages in avoiding local optima and its computational efficiency trade-offs.

Through this paper, readers will gain comprehensive understanding of PSO-optimized text clustering and practical MATLAB implementation skills, including code optimization techniques and parameter tuning strategies. This work provides valuable insights for researchers and practitioners in text mining applications.