A Comparative Analysis of Particle Swarm Optimization and K-Means Algorithm for Text Clustering Using Nepali Wordnet

Sarkar, Sunita; Roy, Arindam; Purkayastha, Bipul Syam

doi:10.5121/ijnlc.2014.3308

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…In 2014, Sunita Sarkar [5] have proposed a hybrid PSO + k-means algorithm and compared it with the kmeans and PSO algorithm. The hybrid approach at first executes the PSO algorithm and the consequences of the PSO algorithm is then utilized as initial centroid of the k-means.…”

Section: K-means Methodsmentioning

confidence: 99%

Exploration of Various Clustering Algorithms for Text Mining

Garg¹,

Gupta²

2018

IJEME

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Due to the current encroachments in technology and also sharp lessening of storage cost, huge extents of documents are being put away in repositories for future references. At the same time, it is time consuming as well as costly to recover the user intrigued documents, out of these gigantic accumulations. Searching of documents can be made more efficient and effective if documents are clustered on the premise of their contents. This article uncovers a comprehensive discussion on various clustering algorithm used in text mining alongside their merits, demerits and comparisons. Further, author has likewise examined the key challenges of clustering algorithms being used for effective clustering of documents.

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Section: K-means Methodsmentioning

confidence: 99%

Exploration of Various Clustering Algorithms for Text Mining

Garg¹,

Gupta²

2018

IJEME

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“…Sunita et al presented a comparative analysis of three clustering algorithms, namely KM, PSO, and hybrid PSO plus KM [54]. The performance of the aforementioned algorithms was tested on text in Nepali language.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The performance of the results of the clustering depends significantly on the text classification and the clustering method. This work provided a comprehensive study of three techniques for clustering text documents utilizing WordNet, namely KM, Particle Swarm Optimization (PSO), and hybrid PSO + KM methods [54]. A bag of words is the standard way of describing a text document.…”

Section: Hybrid Clustering Techniquesmentioning

confidence: 99%

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

et al. 2021

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This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.

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“…In Reference [84], analysis of three methods PSO, K-means algorithm, and hybrid algorithm of PSO and K-means for text clustering. The bag of terms used for describing the text documents, which cannot utilize the semantics.…”

Section: K-means Clustering Techniquementioning

confidence: 99%

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

et al. 2020

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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.

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A Comparative Analysis of Particle Swarm Optimization and K-Means Algorithm for Text Clustering Using Nepali Wordnet

Cited by 15 publications

References 14 publications

Exploration of Various Clustering Algorithms for Text Mining

Exploration of Various Clustering Algorithms for Text Mining

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

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