Text Document Clustering for Topic Discovery by Hypergraph Construction

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationThe Ninth International Conference on Advances in Semantic Processing
Publication statusPublished - 2015

Fingerprint

Clustering algorithms
Decomposition
Experiments

Cite this

Lin, W. S., Liu, C. H., & Chiang, I-J. (2015). Text Document Clustering for Topic Discovery by Hypergraph Construction. In The Ninth International Conference on Advances in Semantic Processing

Text Document Clustering for Topic Discovery by Hypergraph Construction. / Lin, Wei San; Liu, Chih Ho; Chiang, I-Jen.

The Ninth International Conference on Advances in Semantic Processing. 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lin, WS, Liu, CH & Chiang, I-J 2015, Text Document Clustering for Topic Discovery by Hypergraph Construction. in The Ninth International Conference on Advances in Semantic Processing.
Lin WS, Liu CH, Chiang I-J. Text Document Clustering for Topic Discovery by Hypergraph Construction. In The Ninth International Conference on Advances in Semantic Processing. 2015
Lin, Wei San ; Liu, Chih Ho ; Chiang, I-Jen. / Text Document Clustering for Topic Discovery by Hypergraph Construction. The Ninth International Conference on Advances in Semantic Processing. 2015.
@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 -