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I: CLUSTERING & CLASSIFICATION:
• Cluster-preserving dimension reduction methods for efficient classification of text data
• Automatic discovery of similar words
• Simultaneous clustering and dynamic keyword weighting for text documents
• Feature selection and document clustering II: INFORMATION EXTRACTION & RETRIEVAL:
• Vector space models for search and cluster mining
• HotMiner—Discovering hot topics from dirty text
• Combining families of information retrieval algorithms using meta-learning III: TREND DETECTION:
• Trend and behavior detection from Web queries
• A survey of emerging trend detection in textual data mining
* Index
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Add Survey of Text Mining I: Clustering, Classification, and Retrieval, Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space , Survey of Text Mining I: Clustering, Classification, and Retrieval to the inventory that you are selling on WonderClubX
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Add Survey of Text Mining I: Clustering, Classification, and Retrieval, Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space , Survey of Text Mining I: Clustering, Classification, and Retrieval to your collection on WonderClub |