Semantic based clustering of web documents

Tsau Young Lin; I. Jen Chiang

doi:10.1109/GRC.2005.1547264

Semantic based clustering of web documents

Tsau Young Lin, I. Jen Chiang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

A new methodology that structures the semantics of a collection of documents into the geometry of a simplicial complex is developed: A primitive concept is represented by a top dimension simplex. and a connected component represents a concept. Based on these structures, documents can be clustered into some meaningful classes. Experiments with three different data sets from web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and Hierarchical Clustering (HAC). This abstract geometric model seems have captured the intrinsic semantics of the documents.

Original language	English
Title of host publication	2005 IEEE International Conference on Granular Computing
Pages	189-192
Number of pages	4
Volume	2005
DOIs	https://doi.org/10.1109/GRC.2005.1547264
Publication status	Published - 2005
Event	2005 IEEE International Conference on Granular Computing - Beijing, China Duration: Jul 25 2005 → Jul 27 2005

Other

Other	2005 IEEE International Conference on Granular Computing
Country/Territory	China
City	Beijing
Period	7/25/05 → 7/27/05

Keywords

Clustering
Document
Polyhedron
Semantics
Web

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1109/GRC.2005.1547264

Cite this

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title = "Semantic based clustering of web documents",

abstract = "A new methodology that structures the semantics of a collection of documents into the geometry of a simplicial complex is developed: A primitive concept is represented by a top dimension simplex. and a connected component represents a concept. Based on these structures, documents can be clustered into some meaningful classes. Experiments with three different data sets from web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and Hierarchical Clustering (HAC). This abstract geometric model seems have captured the intrinsic semantics of the documents.",

keywords = "Clustering, Document, Polyhedron, Semantics, Web",

author = "Lin, {Tsau Young} and Chiang, {I. Jen}",

year = "2005",

doi = "10.1109/GRC.2005.1547264",

language = "English",

isbn = "0780390172",

volume = "2005",

pages = "189--192",

booktitle = "2005 IEEE International Conference on Granular Computing",

note = "2005 IEEE International Conference on Granular Computing ; Conference date: 25-07-2005 Through 27-07-2005",

}

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AU - Lin, Tsau Young

AU - Chiang, I. Jen

PY - 2005

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N2 - A new methodology that structures the semantics of a collection of documents into the geometry of a simplicial complex is developed: A primitive concept is represented by a top dimension simplex. and a connected component represents a concept. Based on these structures, documents can be clustered into some meaningful classes. Experiments with three different data sets from web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and Hierarchical Clustering (HAC). This abstract geometric model seems have captured the intrinsic semantics of the documents.

AB - A new methodology that structures the semantics of a collection of documents into the geometry of a simplicial complex is developed: A primitive concept is represented by a top dimension simplex. and a connected component represents a concept. Based on these structures, documents can be clustered into some meaningful classes. Experiments with three different data sets from web pages and medical literature have shown that the proposed unsupervised clustering approach performs significantly better than traditional clustering algorithms, such as k-means, AutoClass and Hierarchical Clustering (HAC). This abstract geometric model seems have captured the intrinsic semantics of the documents.

KW - Clustering

KW - Document

KW - Polyhedron

KW - Semantics

KW - Web

UR - http://www.scopus.com/inward/record.url?scp=33845344700&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33845344700&partnerID=8YFLogxK

U2 - 10.1109/GRC.2005.1547264

DO - 10.1109/GRC.2005.1547264

M3 - Conference contribution

AN - SCOPUS:33845344700

SN - 0780390172

SN - 9780780390171

VL - 2005

SP - 189

EP - 192

BT - 2005 IEEE International Conference on Granular Computing

T2 - 2005 IEEE International Conference on Granular Computing

Y2 - 25 July 2005 through 27 July 2005

ER -

Semantic based clustering of web documents

Abstract

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Keywords

ASJC Scopus subject areas

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