A hybrid approach for text document clustering using Jaya optimization algorithm

Thirumoorthy, K.; Muneeswaran, K.

doi:10.1016/j.eswa.2021.115040

Cited by 32 publications

(12 citation statements)

References 29 publications

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“…Rao and Saroj minimized the total annual cost problem by using a variant referred to as Elitist-JAYA in the shell-and-tube heat exchangers design [64]. Thirumoorthy and Muneeswaran used a hybrid JAYA variant to solve the text clustering problem and compared it with some known meta-heuristic algorithms [65]. Chaudhuri and Sahu proposed a hybrid filter-wrapper approach based on JAYA and tested it on 10 micro-array datasets [66].…”

Section: Related Workmentioning

confidence: 99%

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

Yavuz

2022

IEEE Access

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JAYA algorithm is one a recently developed meta-heuristic algorithm that does not require algorithm-specific parameters. It is an algorithm based on the fact that the solutions always go towards the best when searching. This paper proposes a JAYA variant (JAYA-SIP) with three improvements to the Original JAYA algorithm. It has incorporated the senior learning strategy, the incremental population strategy, and Powell's local search method into JAYA. The improvements were tested with IEEE Congress on Evolutionary Computation (CEC) benchmark set for 30 and 50 dimensions, and the benchmark functions set from a special issue of the Soft Computing journal (SOCO) for 500 and 1000 dimensions. In addition to benchmark sets, the performance of JAYA-SIP was evaluated with nine CEC 2011 real-world test functions. The results of the proposed algorithm are compared with JAYA variants and some meta-heuristic algorithms. According to the results of the experiment and the analysis, the proposed improvements increased the performance of the JAYA algorithm. JAYA-SIP achieved better results than the other algorithms it was compared with.

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Section: Related Workmentioning

confidence: 99%

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

Yavuz

2022

IEEE Access

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“…In 2021, Thirumoorthy and Muneeswaran 22 have improved the “hybrid Jaya optimization algorithm” (HJO) for text document clustering. The Silhouette index was used as a metric for measuring the quantity of a solution.…”

Section: Literature Surveymentioning

confidence: 99%

“…It reduces the system's computational power with data constraints. HJO 22 gives the highest quality clustering in the text document. It is easily led to trap in the local optimum problem.…”

Section: Literature Surveymentioning

confidence: 99%

Intelligent deep learning‐based hierarchical clustering for unstructured text data

Jyothi

Lingamgunta

Eluri

2022

Concurrency and Computation

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Document clustering is a technique used to split the collection of textual content into clusters or groups. In modern days, generally, the spectral clustering is utilized in machine learning domain. By using a selection of text mining algorithms, the diverse features of unstructured content is captured for ensuing in rich descriptions. The main aim of this article is to enhance a novel unstructured text data clustering by a developed natural language processing technique. The proposed model will undergo three stages, namely, preprocessing, features extraction, and clustering. Initially, the unstructured data is preprocessed by the techniques such as punctuation and stop word removal, stemming, and tokenization. Then, the features are extracted by the word2vector using continuous Bag of Words model and term frequency-inverse document frequency. Then, unstructured features are performed by the hierarchical clustering using the optimizing the cut-off distance by the improved sensing area-based electric fish optimization (FISA-EFO). Tuned deep neural network is used for improving the clustering model, which is proposed by same algorithm. Thus, the results reveal that the model provides better clustering accuracy than other clustering techniques while handling the unstructured text data. K E Y W O R D S fitness improved sensing area-based electric fish optimization, hierarchical clustering, tuned deep neural network, unstructured text data clustering INTRODUCTIONGenerally, speech and text data are read by humans easily, but the machine learning and statistical modeling applications have some unstructured data and so, it is necessary to do some alterations in the coded input feature sets. 1 Data clustering is a technique used for splitting the data elements into many groups so that the elements in the same group have the highest similarity. Though, based on the cluster's attributes, there are diverse elements in other groups. The major aim of clustering techniques is to get centroids or cluster centers for characterizing the entire cluster. Few of the clustering techniques were performed and classified from different scenarios such as "density-based methods, grid-based methods, partitioning methods, and hierarchical methods." 2,3 Moreover, the data set is defined as categorical or numerical. The primary statistical features of numeric data are utilized for describing the distance function between data elements. The categorical data is imitated from the qualitative and quantitative data, and then the descriptions are attained from the counts. 4 By using a "textual virtual schematic model" (TVSM), the textual data are assigned in clusters and it follows three steps. Initially, the extraction of unstructured data is carried out from the data source, and then, it is changed into structured data. 5 After that, clustering is implemented on structured data. Finally, the comparison of documents is done for enhancing the performance of the query based on accuracy.The day today's life generates a huge amount of unstructured text...

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“…Moreover, the clustering method can be used for [10] [11] labeling the data. The simple clustering methods are K-Means [12], K-Medoids [13], and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [14].…”

Section: Introductionmentioning

confidence: 99%

Forming Dataset of The Undergraduate Thesis using Simple Clustering Methods

Dharmawan

Candramaya²,

Widharta³

2024

IJIES

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Each university collects many undergraduate theses data but has yet to process it to make it easier for students to find references as desired. This study aims to classify and compare the grouping of documents using expert and simple clustering methods. Experts have done ground truth using OR Boolean Retrieval and keyword generation. The best cluster was discovered by the experiments using the K-Means, K-Medoids, and DBSCAN clustering methods and using Euclidean, Manhattan, City Block, and Cosine Similarity metrics. The cluster with the best Silhouette Score compared to the accuracy of the categorization of each document. The K-Means clustering method and the Cosine Similarity metric gave the best results with a Silhouette Score value of 0.105534. The comparison between ground truth and the best cluster results shows an accuracy of 33.42%. The result shows that the simple clustering method cannot handle data with Negative Skewness and Leptokurtic Kurtosis.

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A hybrid approach for text document clustering using Jaya optimization algorithm

Cited by 32 publications

References 29 publications

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

Intelligent deep learning‐based hierarchical clustering for unstructured text data

Forming Dataset of The Undergraduate Thesis using Simple Clustering Methods

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