Algorithm for Text Clustering Optimized with PSO

Text clustering optimized with PSO is an efficient text analysis technique combining swarm intelligence with unsupervised learning. By integrating Particle Swarm Optimization (PSO) with traditional clustering methods like K-means, this algorithm dynamically optimizes cluster centroid positions, significantly improving classification accuracy for text data.

The core methodology operates through three key phases:

Text Vectorization First, text documents are converted into numerical vectors using TF-IDF or word embedding techniques, constructing a high-dimensional feature space. In MATLAB, this can be implemented using the Text Analytics Toolbox for term frequency counting and dimensionality reduction operations such as PCA or latent semantic analysis.

PSO Parameter Optimization Each particle in the swarm represents a set of potential cluster centroids. Through iterative updates of particle positions (centroid coordinates), the algorithm evaluates fitness functions like silhouette coefficient or within-cluster distances. MATLAB's Global Optimization Toolbox provides efficient implementations for updating particle velocity and position using vectorized operations and parallel processing capabilities.

Hybrid Clustering Execution The optimal centroids output by PSO serve as initial seeds for K-means clustering, eliminating local optimum issues caused by random initialization in traditional methods. Final clustering results can be visualized using MATLAB's plotting tools like scatter plots for 2D/3D projections or heatmaps for cluster density patterns.

Key advantages of this approach include: Better adaptation to non-convex text data distributions Reduced sensitivity of clustering results to initial values Acceleration of particle evaluation through MATLAB's parallel computing capabilities

Typical enhancement directions involve dynamic adjustment of inertia weights, integration with other evolutionary algorithms for multi-objective optimization, and implementation of adaptive neighborhood topologies for improved convergence properties.