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The Scientific World Journal
Volume 2014 (2014), Article ID 784392, 9 pages
Research Article

Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received 16 October 2013; Accepted 8 January 2014; Published 23 February 2014

Academic Editors: R. Valencia-García and Y.-B. Yuan

Copyright © 2014 Jin Dai and Xin Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The similarity between objects is the core research area of data mining. In order to reduce the interference of the uncertainty of nature language, a similarity measurement between normal cloud models is adopted to text classification research. On this basis, a novel text classifier based on cloud concept jumping up (CCJU-TC) is proposed. It can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers.