Abstract
Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.
Highlights
Text document clustering is the application of cluster analysis referring to the unsupervised classification of textual documents into clusters based on content similarity
We used standard particle swarm optimization (PSO), bat algorithm (BA), and grey wolf optimization (GWO) for the text document clustering with six data sets, and the performance of these algorithms was evaluated using various metrics such as purity, homogeneity, completeness, V-measure, adjusted rand index (ARI), and average running time
The specific characteristics of each Swarm intelligence (SI) algorithm are suitable for solving specific optimization problems such as feature selection, finding an optimal route, job scheduling, role-based learning, and clustering
Summary
Text document clustering is the application of cluster analysis referring to the unsupervised classification of textual documents into clusters based on content similarity. Text document clustering can be applied in organizing large document collections, extracting hidden information from data generated by IoT sensors, finding similar documents, detecting duplicate content, search optimization, and recommendation systems [1,2]. These text document sources may come from web pages, blog posts, news articles, or other text files [3]. Extracting relevant information from the data is a challenging task that needs fast and high-quality document clustering algorithms.
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