Text Document Clustering for Topic Discovery by Hypergraph Construction

Wei San Lin; Chih Ho Liu; I-Jen Chiang

Text Document Clustering for Topic Discovery by Hypergraph Construction

Wei San Lin, Chih Ho Liu, I-Jen Chiang

醫學資訊研究所

研究成果: 書貢獻/報告類型 › 會議貢獻

摘要

The paper presents a hypergraph model and HYPERGRAPH DECOMPOSITION ALGORITHM for text document clustering. The experiments on three different data sets from news, Web, and medical literatures have shown our algorithm is significantly better than traditional clustering algorithms, such as K-MEANS, PRINCIPAL DIRECTION DIVISIVE PARTITIONING , AUTOCLASS and HIERACHICAL CLUSTERING.

原文	英語
主出版物標題	The Ninth International Conference on Advances in Semantic Processing
出版狀態	已發佈 - 2015

引用此

@inproceedings{c391ffca467c4896829d0602491cbbc6,

title = "Text Document Clustering for Topic Discovery by Hypergraph Construction",

abstract = "The paper presents a hypergraph model and HYPERGRAPH DECOMPOSITION ALGORITHM for text document clustering. The experiments on three different data sets from news, Web, and medical literatures have shown our algorithm is significantly better than traditional clustering algorithms, such as K-MEANS, PRINCIPAL DIRECTION DIVISIVE PARTITIONING , AUTOCLASS and HIERACHICAL CLUSTERING.",

author = "Lin, {Wei San} and Liu, {Chih Ho} and I-Jen Chiang",

year = "2015",

language = "English",

booktitle = "The Ninth International Conference on Advances in Semantic Processing",

}

TY - GEN

T1 - Text Document Clustering for Topic Discovery by Hypergraph Construction

AU - Lin, Wei San

AU - Liu, Chih Ho

AU - Chiang, I-Jen

PY - 2015

Y1 - 2015

N2 - The paper presents a hypergraph model and HYPERGRAPH DECOMPOSITION ALGORITHM for text document clustering. The experiments on three different data sets from news, Web, and medical literatures have shown our algorithm is significantly better than traditional clustering algorithms, such as K-MEANS, PRINCIPAL DIRECTION DIVISIVE PARTITIONING , AUTOCLASS and HIERACHICAL CLUSTERING.

AB - The paper presents a hypergraph model and HYPERGRAPH DECOMPOSITION ALGORITHM for text document clustering. The experiments on three different data sets from news, Web, and medical literatures have shown our algorithm is significantly better than traditional clustering algorithms, such as K-MEANS, PRINCIPAL DIRECTION DIVISIVE PARTITIONING , AUTOCLASS and HIERACHICAL CLUSTERING.

M3 - Conference contribution

BT - The Ninth International Conference on Advances in Semantic Processing

ER -

Text Document Clustering for Topic Discovery by Hypergraph Construction

摘要

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