Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature

Jayabharathy, J.; Kanmani, S.

doi:10.1186/2193-8636-1-3

Cited by 14 publications

(10 citation statements)

References 40 publications

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“…The proposed topic updation takes this idea of considering crtv as concepts for static clustering and topic detection and applies the same concept for clustering and updating topic to the document clusters dynamically. Considering terms or synonyms and hypernyms for information extraction leads the following issues [25]:…”

Section: Need For Correlated Terms [25]mentioning

confidence: 99%

“…But using the proposed model the concept will be extracted as "farmer", "crops", "fertilizer", "land" and "farm". Clustering the document using this extraction procedure would improve the performance of the resulting cluster, than that of the cluster generated by existing works [25].…”

Section: Whereas Using Correlated Concept Extraction Algorithm the Tementioning

confidence: 99%

“…At first the new documents are preprocessed and then it is clustered based on the dynamic technique. The issues that are to be addressed are [25]:…”

Section: Dynamic Document Clusteringmentioning

confidence: 99%

“…The PSSM which is based on single word analysis and phrase analysis, assigns and adjusts the term weight (word/phrase) based on its relationships with semantically similar terms that occur together in the document. As soon as the new document is incrementally added to the cluster, the semantic histogram ratio is calculated and the insertion order problem is addressed by making bad documents that reduce the cluster cohesiveness to leave, and reassign them to a more appropriate cluster [25].…”

Section: Semantic Similarity Histogram Based Incremental Document Clumentioning

confidence: 99%

“…Adding the new document to this cluster (when possible) will probably give the greatest benefit to that cluster and the entire dataset. This approach uses the same pair-wise document similarity representation and distribution approach and also uses additional information about the cluster to determine the best cluster to place the new document [25].…”

Section: Enhanced Similarity Histogram Clustering Using Intra Centroimentioning

confidence: 99%

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Correlated Concept based Topic Updation Model for Dynamic Corpora

Jayabharathy¹,

Kanmani²,

Sivaranjani³

2014

IJCA

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A rapid growth of documents available on the Internet, digital libraries, medical documents, news wires and other scientific document corpuses has motivated the researchers to propose many text mining techniques that help users to quickly retrieve trace and summarize the information in an effective way. Topic detection is one such technique which discovers precise, meaningful and concise labels for the formulated static document clusters. This technique helps the user to navigate and retrieve the needed information quickly and efficiently. Topic updation is the process of identifying and renewing the discovered labels whenever the document clusters are updated dynamically. This paper focuses on topic updation model based on Testor theory. The proposed work is experimented using 20newsgroup and scientific literature data set. The experimental results demonstrate that the proposed algorithm exhibit better performance, compared to the existing algorithms for topic detection.

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Section: Need For Correlated Terms [25]mentioning

confidence: 99%

Section: Whereas Using Correlated Concept Extraction Algorithm the Tementioning

confidence: 99%

“…At first the new documents are preprocessed and then it is clustered based on the dynamic technique. The issues that are to be addressed are [25]:…”

Section: Dynamic Document Clusteringmentioning

confidence: 99%

Section: Semantic Similarity Histogram Based Incremental Document Clumentioning

confidence: 99%

Section: Enhanced Similarity Histogram Clustering Using Intra Centroimentioning

confidence: 99%

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Correlated Concept based Topic Updation Model for Dynamic Corpora

Jayabharathy¹,

Kanmani²,

Sivaranjani³

2014

IJCA

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Iterative Concept-Based Clustering of Indian Court Judgments

Mathai

Gupta

Radhakrishnan

2018

Proceedings of the Second International Conference on Computational Intelligence and Informatics

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Text-document clustering-based cause and effect analysis methodology for steel plant incident data

Verma

Maiti

2018

International Journal of Injury Control and Safety Promotion

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The purpose of this study is to develop a text clustering-based cause and effect analysis methodology for incident data to unfold the root causes behind the incidents. A cause-effect diagram is usually prepared by using experts' knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. The study also helped in finding out the anomaly in incident reporting.

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Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature

Cited by 14 publications

References 40 publications

Correlated Concept based Topic Updation Model for Dynamic Corpora

Correlated Concept based Topic Updation Model for Dynamic Corpora

Iterative Concept-Based Clustering of Indian Court Judgments

Text-document clustering-based cause and effect analysis methodology for steel plant incident data

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