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Text Mining: Classification, Clustering, and Applications by Ashok Srivastava, Mehran Sahami

Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami ebook
Publisher: Chapman & Hall
Format: pdf
ISBN: 1420059408, 9781420059403
Page: 308

Text-mining approaches typically rely on occurrence and co-occurrence statistics of terms and have been successfully applied to a number of problems. Here are some of the open source NLP and machine learning tools for text mining, information extraction, text classification, clustering, approximate string matching, language parsing and tagging, and more. Moreover, developers of text or literature mining applications are working at a furious pace, in part because mapping the human genome led to an explosion of text-based genetic information. Two basic TM tasks are classification and clustering of retrieved documents. (Genomics refers to the molecular pathways); and (c) text mining to find "non-trivial, implicit, previously unknown" patterns (p. As a result, several large and complicated genomics and proteomics databases exist. Text Mining: Classification, Clustering, and Applications book download. This is a detailed survey book on text mining, which discusses the classical key topics, including clustering, classification, and dimensionality reduction; and emerging topics such as social networks, multimedia and transfer. Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Download Text Mining: Classification, Clustering, and Applications text mining is needed when “words are not enough.†This book:.