Enhancing Document Clustering Using Condensing Cluster Terms and Fuzzy Association

Sun PARK; Seong Ro LEE

doi:10.1587/transinf.E94.D.1227

Enhancing Document Clustering Using Condensing Cluster Terms and Fuzzy Association

Sun PARK, Seong Ro LEE

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Most document clustering methods are a challenging issue for improving clustering performance. Document clustering based on semantic features is highly efficient. However, the method sometimes did not successfully cluster some documents, such as highly articulated documents. In order to improve the clustering success of complex documents using semantic features, this paper proposes a document clustering method that uses terms of the condensing document clusters and fuzzy association to efficiently cluster specific documents into meaningful topics based on the document set. The proposed method improves the quality of document clustering because it can extract documents from the perspective of the terms of the cluster topics using semantic features and synonyms, which can also better represent the inherent structure of the document in connection with the document cluster topics. The experimental results demonstrate that the proposed method can achieve better document clustering performance than other methods.

Publication: IEICE TRANSACTIONS on Information Vol.E94-D No.6 pp.1227-1234

Publication Date: 2011/06/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E94.D.1227

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

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Sun PARK, Seong Ro LEE, "Enhancing Document Clustering Using Condensing Cluster Terms and Fuzzy Association" in IEICE TRANSACTIONS on Information, vol. E94-D, no. 6, pp. 1227-1234, June 2011, doi: 10.1587/transinf.E94.D.1227.
Abstract: Most document clustering methods are a challenging issue for improving clustering performance. Document clustering based on semantic features is highly efficient. However, the method sometimes did not successfully cluster some documents, such as highly articulated documents. In order to improve the clustering success of complex documents using semantic features, this paper proposes a document clustering method that uses terms of the condensing document clusters and fuzzy association to efficiently cluster specific documents into meaningful topics based on the document set. The proposed method improves the quality of document clustering because it can extract documents from the perspective of the terms of the cluster topics using semantic features and synonyms, which can also better represent the inherent structure of the document in connection with the document cluster topics. The experimental results demonstrate that the proposed method can achieve better document clustering performance than other methods.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1227/_p

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@ARTICLE{e94-d_6_1227,
author={Sun PARK, Seong Ro LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Enhancing Document Clustering Using Condensing Cluster Terms and Fuzzy Association},
year={2011},
volume={E94-D},
number={6},
pages={1227-1234},
abstract={Most document clustering methods are a challenging issue for improving clustering performance. Document clustering based on semantic features is highly efficient. However, the method sometimes did not successfully cluster some documents, such as highly articulated documents. In order to improve the clustering success of complex documents using semantic features, this paper proposes a document clustering method that uses terms of the condensing document clusters and fuzzy association to efficiently cluster specific documents into meaningful topics based on the document set. The proposed method improves the quality of document clustering because it can extract documents from the perspective of the terms of the cluster topics using semantic features and synonyms, which can also better represent the inherent structure of the document in connection with the document cluster topics. The experimental results demonstrate that the proposed method can achieve better document clustering performance than other methods.},
keywords={},
doi={10.1587/transinf.E94.D.1227},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Enhancing Document Clustering Using Condensing Cluster Terms and Fuzzy Association
T2 - IEICE TRANSACTIONS on Information
SP - 1227
EP - 1234
AU - Sun PARK
AU - Seong Ro LEE
PY - 2011
DO - 10.1587/transinf.E94.D.1227
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2011
AB - Most document clustering methods are a challenging issue for improving clustering performance. Document clustering based on semantic features is highly efficient. However, the method sometimes did not successfully cluster some documents, such as highly articulated documents. In order to improve the clustering success of complex documents using semantic features, this paper proposes a document clustering method that uses terms of the condensing document clusters and fuzzy association to efficiently cluster specific documents into meaningful topics based on the document set. The proposed method improves the quality of document clustering because it can extract documents from the perspective of the terms of the cluster topics using semantic features and synonyms, which can also better represent the inherent structure of the document in connection with the document cluster topics. The experimental results demonstrate that the proposed method can achieve better document clustering performance than other methods.
ER -