Title page for etd-0826109-151344


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URN etd-0826109-151344
Author Ren-jia Liu
Author's Email Address No Public.
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Department Electrical Engineering
Year 2008
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title A Self-Constructing Fuzzy Feature Clustering for Text Categorization
Date of Defense 2009-07-22
Page Count 75
Keyword
  • text classification
  • feature reduction
  • feature clustering
  • feature extraction
  • fuzzy clustering
  • fuzzy similarity
  • Abstract Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering. The words in the feature vector of a document set are grouped into clusters based on similarity test. Words that are similar to each other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical mean and deviation. When all the words have been fed in, a desired number of clusters are formed automatically. We then have one extracted feature for each cluster. The extracted feature corresponding to a cluster is a weighted combination of the words contained in the cluster.
    By this algorithm, the derived membership functions match closely with and describe properly the real distribution of the training data. Besides, the user need not specify the number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted features can then be avoided. 20 Newsgroups data set and Cade 12 web directory are introduced to be our experimental data. We adopt the support vector machine to classify the documents. Experimental results show that our method can run faster and obtain better extracted features than other methods.
    Advisory Committee
  • Tzung-Pei Hong - chair
  • Wen-Yang Lin - co-chair
  • Chaur-Heh Hsieh - co-chair
  • Chung-Ming Kuo - co-chair
  • Shie-Jue Lee - advisor
  • Files
  • etd-0826109-151344.pdf
  • indicate access worldwide
    Date of Submission 2009-08-26

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