Text clustering system is a proper technique which mainly segments large measure of textual documents into clusters. The size of the material influences the clustering of text by reducing its performance. In this manner, the textual document comprises sparse and uninformative features, and thus raises the computational time and decreases the execution of primary clustering process. Feature selection is a crucial system to choose another subset of instructive text feature to enhance text clustering execution and diminish computational time. The implemented model proposes a 2logmean-particle swarm optimization algorithm for the unstructured text clustering. In this newly proposed technique, all the texts are initially converted into ASCII value, and then by using the particle swarm optimization, the document text is clustered. The outcomes display that clustering accuracy of the implemented method is high compared to the existing K-means algorithm. Furthermore, performances of newly implemented techniques are evaluated concerning scalability, less computation speed with colossal dimensionality reduction.
A Comparative Analysis of Particle Swarm Optimization and K-Means Algorithm for Text Clustering Using Nepali Wordnet