Genetic algorithm for text clustering based on latent semantic indexing

Song, Wei; Park, Soon Cheol

doi:10.1016/j.camwa.2008.10.010

Cited by 75 publications

(34 citation statements)

References 12 publications

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“…But GA cannot be applied to high dimensional VSM representational model of documents due to scalability issue and high computational cost. So, in [26] GA method based on a latent semantic model for text clustering is presented. LSI uses SVD technique to decompose the large term-bydocument matrix into a set of k orthogonal factors.…”

Section: Review Of Semantic Driven Document Clustering Methodsmentioning

confidence: 99%

Semantic based Document Clustering: A Detailed Review

Shah¹,

Mahajan²

2012

IJCA

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Section: Review Of Semantic Driven Document Clustering Methodsmentioning

confidence: 99%

Semantic based Document Clustering: A Detailed Review

Shah¹,

Mahajan²

2012

IJCA

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“…Hybridization can be applied to improve the accuracy of LSA. Genetic algorithms can be applied to reduce the dimensions of the matrix [10]. LSA method is used as the basic method of building automatic graders because it is able to compare the meaning of student answers and key answers.…”

Section: Latent Semantic Analysis (Lsa)mentioning

confidence: 99%

Pre-Processed Latent Semantic Analysis for Automatic Essay Grading

Febrita¹,

Mahmudy²

2017

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“…Genetic technique [34] is search techniques that related to the principle of natural selection. Clustering is a well-known unsupervised pattern categorization method that divides input space into K regions based on similarity/dissimilarity metric.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Evaluation of Clustering around Weighted Prototype and Genetic Algorithm for Document Categorization

Jain¹,

Shrivastava²

2015

IJCA

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Document clustering is very important in the field of text categorization. Genetic algorithm, which is an optimization based technique which can be applied for finding out the best cluster centres easily by computing fitness values of data points. While clustering around weighted prototype technique is especially helpful when proper pairwise similarities are available. This technique does not find global solution of the objective function. Experimental result shows that F-measure and Normalized mutual information of genetic algorithm is better than clustering around weighted prototype for 20 Newsgroup dataset. F-measure and accuracy of genetic algorithm is better than clustering around weighted prototype for the Reuter-21578 dataset.

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Genetic algorithm for text clustering based on latent semantic indexing

Cited by 75 publications

References 12 publications

Semantic based Document Clustering: A Detailed Review

Semantic based Document Clustering: A Detailed Review

Pre-Processed Latent Semantic Analysis for Automatic Essay Grading

Evaluation of Clustering around Weighted Prototype and Genetic Algorithm for Document Categorization

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