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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

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. Text-mining approaches typically rely on occurrence and co-occurrence statistics of terms and have been successfully applied to a number of problems. Survey of Text Mining I: Clustering, Classification, and Retrieval Publisher: Springer | ISBN: 0387955631 | edition 2003 | PDF | 262 pages | 13,1 mb Survey of Text Mining I: Clustering, Cla. Whether or not the algorithm divides a set in successive binary splits, aggregates into overlapping or non-overlapping clusters. Weak Signals and Text Mining II - Text Mining Background and Application Ideas. 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. And Lafferty, J.D., “Topic Models”, Text mining: classification, clustering, and applications., 2009, pp. Etc will tend to give slightly different results. A text mining example is the classification of the subject of a document given a training set of documents with known subjects. Computational pattern discovery and classification based on data clustering plays an important role in these applications. Srivastava is the author of many research articles on data mining, machine learning and text mining, and has edited the book, “Text Mining: Classification, Clustering, and Applications” (with Mehran Sahami, 2009). Unsupervised methods can take a range of forms and the similarity to identify clusters.