Text documents clustering using modified multi-verse optimizer

Abstract

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems.
 For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.

An Improved Multi-Verse Optimizer for Text Documents Clustering

“…Since 2016, various attempts have been carried out and numerous variants have, accordingly, been proposed with the aim of improving the MVO performance (Abasi et al, 2020d). Also, based on the theorem of no free lunch, there does not exist an equally successful global optimizer, which can provide solutions to all the optimization problems.…”

“…The neighbors of a solution Ui in the population matrix are the solutions, which are similar to Abasi et al, (2020d). Let sim (Ui,Uj) represents a similarity function capturing the pairwise similarity between two solutions, Ui,Uj, having values between 0 and 1, with a larger value to indicate higher similarity.…”

A Hybrid Artificial Intelligence Model for Detecting Keratoconus

“…Clustering algorithms try to group similar samples in the same cluster. As such, possible solution (to the problem) and a fitness function to evaluate the solution are two major components in the clustering process [45][46][47]. The possible solution is the first step after pre-processing phase is providing feasible solutions for a particular problem while the fitness function evaluates these solutions in the clustering process.…”

Classifying clinically actionable genetic mutations using KNN and SVM

“…Reference [20] and [21] discuss the usage of metaheuristic algorithms after fuzzy modelling the problem. Abasi et al develop a cluster-based approach for text documents by treating text document clustering as discrete optimization problem [22]. Rustam et al propose a new feature selection method and use K-means clustering as the classifier using radial basis function and polynomial kernel function [23].…”