Paper Title:
Document Clustering Based on Fuzzy Similarity
  Abstract

This paper proposes a novel fuzzy similarity measure based on the relationships between terms and categories. A term-category matrix is presented to represent such relationships and each element in the matrix denotes a membership degree that a term belongs to a category, which is computed using term frequency inverse document frequency and fuzzy relationships between documents and categories. Fuzzy similarity takes into account the situation that one document belongs to multiple categories and is computed using fuzzy operators. The experimental results show that the proposed fuzzy similarity surpasses other common similarity measures not only in the reliable derivation of document clustering results, but also in document clustering accuracies.

  Info
Periodical
Edited by
Honghua Tan
Pages
2620-2626
DOI
10.4028/www.scientific.net/AMM.29-32.2620
Citation
J. L. Zhou, X. J. Nie, L. H. Qin, J. F. Zhu, "Document Clustering Based on Fuzzy Similarity", Applied Mechanics and Materials, Vols. 29-32, pp. 2620-2626, 2010
Online since
August 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Fang Li, Qun Xiong Zhu
Abstract:LSI based hierarchical agglomerative clustering algorithm is studied. Aiming to the problems of LSI based hierarchical agglomerative...
1306
Authors: Hong Fei Li, Fu Ling Wang, Shi Jue Zheng, Li Gao
Abstract:The fuzzy clustering algorithm is sensitive to the m value and the degree of membership. Because of the deficiencies of traditional FCM...
545
Authors: Ying Zhao, Ya Jun Du, Qiang Qiang Peng
Abstract:Clustering web search results is a kind of solution which help user to find the interested topic by grouping the search results. This paper...
1418
Authors: Yin Sheng Zhang, Hui Lin Shan, Jia Qiang Li, Jie Zhou
Chapter 8: Nanomaterials and Nanomanufacturing
Abstract:The traditional K-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster...
1977
Authors: Chun Xia Jin, Hai Yan Zhou, Qiu Chan Bai
Chapter 6: Algorithm Design
Abstract:To solve the problem of sparse keywords and similarity drift in short text segments, this paper proposes short text clustering algorithm with...
1716