[1] Ji Xianghua, Chen Chao, Shao Zhengrong, Yu Nenghai(MOE-MS Key Laboratory of Multimedia Computing and Communication, et al. Fuzzy c-means text clustering based on topic concept sub-space [J]. Journal of Southeast University (English Edition), 2007, 23 (3): 439-442. [doi:10.3969/j.issn.1003-7985.2007.03.028]
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Fuzzy c-means text clustering based on topic concept sub-space(
)
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 23
- Issue:
- 2007 3
- Page:
- 439-442
- Research Field:
- Computer Science and Engineering
- Publishing date:
- 2007-09-30
Info
- Title:
- Fuzzy c-means text clustering based on topic concept sub-space
- Author(s):
- Ji Xianghua1; Chen Chao2; Shao Zhengrong2; Yu Nenghai1(1MOE-MS Key Laboratory of Multimedia Computing and Communication; University of Science and Technology of China; Hefei 230027; China)
- 2Library, University of Science and Technology of China, Hefei 230027, China
- PACS:
- TP391
- DOI:
- 10.3969/j.issn.1003-7985.2007.03.028
- Abstract:
- To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space(TCS2FCM)is introduced for classifying texts.Five evaluation functions are combined to extract key phrases.Concept phrases, as well as the descriptions of final clusters, are presented using WordNet○R origin from key phrases.Initial centers and membership matrix are the most important factors affecting clustering performance.Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces.The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.
References:
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Memo
- Memo:
- Biographies: Ji Xianghua(1982—), male, graduate;Yu Nenghai(corresponding author), male, doctor, professor, ynh@ustc.edu.cn.