Text Categorization Research Based on Cluster Idea

Lin, Jialun; Li, Xiaoling; Jiao, Yuan

doi:10.1109/etcs.2010.413

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Jialun Lin, Xiaoling Li and Yuan Jiao applied cluster idea to text categorization [4]. They attempted to use the text categorization pattern of self-initiated learning to design a clustering-based text categorization algorithm, in the purpose of reducing the dimension of training set and raising the efficiency of categorization implementation.…”

Section: Related Workmentioning

confidence: 99%

Enriched Format Text Categorization Using A Component Similarity Approach

Zhu

Yang²,

Yong³

2011

JSW

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Text categorization has been widely studied for years. However, conventional plain text categorization approaches which work good in plain text behave poor when they are simply applied to enriched format texts. An categorization approach that is applicable to enriched format text is proposed. During feature selection, we get feature structure distribution weight by using extended structure model so that structure affections to categorization are fully considered. Text formats are also taken into account in feature weighting. The combined feature weighting approach strengthens important parts and weakens less important ones. The text categorization is fulfilled by document component similarity, which first decomposes document, gathers features by components and other user-defined rules, completes document component tree, and then achieves categorization by it. We implement a CSBC based Naïve Bayes classifier in which the final result is the combination of all classifiers of component tree. Finally we parse OpenOffice.org document, draw components that are most related to classification from OpenOffice.org documents, and then use the classifier to categorize OpenOffice.org documents. The experiment results show that the classifier can automatically classify OpenOffice.org documents and work quite well

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Section: Related Workmentioning

confidence: 99%

Enriched Format Text Categorization Using A Component Similarity Approach

Zhu

Yang²,

Yong³

2011

JSW

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Text Categorization By Content using Naïve Bayes Approach

Dawar

Kumar

2023

2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON)

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A Bayesian Network Based Intelligent Plant Classification System

Xia

2012

2012 Fourth International Symposium on Information Science and Engineering

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We design a convenience way in plant research, which can classify the hibernal plants automatically. Different features related to this model will be taken into consideration, and other influenced facts will be included as well. The main points of the classification should be picked out and we will implement the system according to them and on the basis of Bayes. The Bayes Network is in use of coefficient learning so that we can get the advantages of adaptive learning and the best effects of classification through self-learning and adjusting parameters according to actual data. In this way, the problem of classifying hibernal plant automatically is solved and the result of experiment will be showed in the paper.

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Text Categorization Research Based on Cluster Idea

Cited by 4 publications

References 5 publications

Enriched Format Text Categorization Using A Component Similarity Approach

Enriched Format Text Categorization Using A Component Similarity Approach

Text Categorization By Content using Naïve Bayes Approach

A Bayesian Network Based Intelligent Plant Classification System

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