Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans

Chien-Liang Liu; Tao-Hsing Chang; Hsuan-Hsun Li

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Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans

机译：使用模糊半均值将文档与带标签和未带标签的文档聚类

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摘要

While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. Additionally, the fuzzy semi-Kmeans provides the flexibility to employ different fuzzy membership functions to measure the distance between data. This work employs Gaussian weighting function to conduct experiments, but cosine similarity function can be used as well. This work conducts experiments on three data sets and compares fuzzy semi-Kmeans with several methods. The experimental results indicate that fuzzy semi-Kmeans can generally outperform the other methods.

机译：在关注文档聚类的同时，这项工作提出了一种称为模糊半Kmeans的模糊半监督聚类算法。模糊半Kmeans是K-means聚类模型的扩展，它受EM算法和高斯混合模型的启发。此外，模糊半Kmeans提供了使用不同模糊隶属度函数来测量数据之间距离的灵活性。这项工作采用高斯加权函数进行实验，但也可以使用余弦相似度函数。这项工作对三个数据集进行了实验，并用几种方法比较了模糊半均值。实验结果表明，模糊半均值算法通常可以胜过其他方法。

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来源
《Fuzzy sets and systems》 |2013年第16期|48-64|共17页
作者
Chien-Liang Liu; Tao-Hsing Chang; Hsuan-Hsun Li;
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作者单位

Information and Communications Research Laboratories, Industrial Technology Research Institute, Rm. 709, Bldg. 51, 195, Sec. 4, Chung Hsing Rd., Chiming, Hsinchu 310, Taiwan, ROC;

Department of Computer Science and Information Engineering, National Kaohsiung University of Applied Sciences, Chien Kung Campus 415, Chien Kung Road, Kaohsiung 807, Tainan, ROC;

Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan, ROC;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
fuzzy clustering; semi-supervised learning; text mining; fuzzy semi-Kmeans;

机译：模糊聚类半监督学习;文本挖掘;模糊半均值;

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3. A modified fuzzy clustering for documents retrieval: Application to document categorization [J] . S. Nefti, Y. Rezgui, M. Oussalah Operations Research . 2010,第1a2期

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4. Text Document Topical Recursive Clustering and Automatic Labeling of a Hierarchy of Document Clusters [C] . Xiaoxiao Li, Jiyang Chen, Osmar Zaiane Pacific-Asia conference on knowledge discovery and data mining . 2013

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5. Text document topical recursive clustering and automatic labeling of a hierarchy of document clusters. [D] . Li, Xiaoxiao. 2012

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6. Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals [O] . Hamed Hassanzadeh, Mahnoosh Kholghi, Anthony Nguyen, 2018

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7. Text Document Topical Recursive Clustering and Automatic Labeling of a Hierarchy of Document Clusters [O] . Xiaoxiao Li, Jiyang Chen, Osmar Zaiane 2013

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8. Using EM to Classify Text from Labeled and Unlabeled Documents [R] . Nigam, K. , McCallum, A. , Thrun, S. , 1998

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Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans

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