Documents Clustering Using Tolerance Rough Set Model and Its Application to Information Retrieval

Ho, Tu Bao; Kawasaki, Shigeo; Nguyen, Ngoc Binh

doi:10.1007/978-3-7908-1772-0_12

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…You can find more information in [25], [26] and [27]. We have a set of N journals (we know all topics for every journal which the journal contains).…”

Section: E the Tolerance Rough Set Clustering Algorithmmentioning

confidence: 99%

A Tolerance Rough Set Based Overlapping Clustering for the DBLP Data

Obadi

Dráždilová

Hlavacek

et al. 2010

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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In the article there is presented comparison of overlapping clustering methods for data mining of DBLP datasets. For the analysis, the DBLP data sets were pre-processed, while each journal has been assigned attributes, defined by its topics. The data collection can be described as vague and uncertain; obtained clusters and applied queries do not necessarily have crisp boundaries. The authors presented clustering through a tolerance rough set method (TRSM) and fuzzy c-mean (FCM) algorithm for journal recommendation based on topic search. The comparison of both clustering methods was presented using different measures of similarity.

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“…You can find more information in [25], [26] and [27]. We have a set of N journals (we know all topics for every journal which the journal contains).…”

Section: E the Tolerance Rough Set Clustering Algorithmmentioning

confidence: 99%

A Tolerance Rough Set Based Overlapping Clustering for the DBLP Data

Obadi

Dráždilová

Hlavacek

et al. 2010

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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“…In Ho et al (2006), the authors describe a method to cluster documents using tolerance rough sets. In Ho et al (2006), the authors describe a method to cluster documents using tolerance rough sets.…”

Section: Document Clusteringmentioning

confidence: 99%

On rough sets, their recent extensions and applications

MacParthaláin

Shen

2010

The Knowledge Engineering Review

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Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has recently been carried out in respect of both the development of the underlying theory and the application to new problem domains. This paper attempts to summarize the advances in RST, its extensions, and their applications. It also identifies important areas which require further investigation. Typical example application domains are examined which demonstrate the success of the application of RST to a wide variety of areas and disciplines, and which also exhibit the strengths and limitations of the respective underlying approaches.

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“…Previous works on Vietnamese text clustering have used tolerance rough set models [13], Self Organizing Map (SOM) [7], Named entity based Multi Vector space model [3,4] in developing automated text clustering system. Almost these systems use vector space model for document representation, required good word segmentation step and are not suitable to be used in an incrementally growing set of documents.…”

Section: Related Workmentioning

confidence: 99%

Efficient approach for incremental Vietnamese document clustering

Hoang

2009

Proceedings of the Eleventh International Workshop on Web Information and Data Management

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In this paper, we present how to use graph model for clustering Vietnamese document incrementally. Graph based model allows us to model completely the structure of not only each document but also the whole collection of documents. The graph structure is easily updated when there is a new document. When building the graph incrementally we can identify representative subgraph features, which are later used for calculating hybrid pair-wise document similarity. These subgraph features make clustering process less sensitive to the Vietnamese word segmentation step. Based on the hybrid similarity measure, the documents are groups into clusters on-the-fly without any assumptions on the number of clusters and without retrieving previous documents.

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Documents Clustering Using Tolerance Rough Set Model and Its Application to Information Retrieval

Cited by 10 publications

References 6 publications

A Tolerance Rough Set Based Overlapping Clustering for the DBLP Data

A Tolerance Rough Set Based Overlapping Clustering for the DBLP Data

On rough sets, their recent extensions and applications

Efficient approach for incremental Vietnamese document clustering

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