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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
ISBN: 1420059408, 9781420059403
Page: 308
Format: pdf
Publisher: Chapman & Hall

But they're not random: errors cluster in certain words and periods. Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami. Text-mining approaches typically rely on occurrence and co-occurrence statistics of terms and have been successfully applied to a number of problems. Link to MnCat Record · Read about this book on Amazon Text mining : classification, clustering, and applications. EbooksFreeDownload.org is a free ebooks site where you can download free books totally free. Text Mining: Classification, Clustering, and Applications book download. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. And Lafferty, J.D., “Topic Models”, Text mining: classification, clustering, and applications., 2009, pp. Srivastava, Ashok N., Sahami, Mehran. 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. Computational pattern discovery and classification based on data clustering plays an important role in these applications. Text Mining: Classification, Clustering, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) Author - Ashok Srivastava, Mehran Sahami. Issues relating to interoperability, information silos and access restrictions are limiting the uptake, degree of automation and potential application areas of text mining. 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). But it has probably been the single most influential application of text mining, so clearly people are finding this simple kind of diachronic visualization useful. Text Mining: Classification, Clustering, and Applications. B) (Supervised) classification: a program can learn to correctly distinguish texts by a given author, or learn (with a bit more difficulty) to distinguish poetry from prose, tragedies from history plays, or “gothic novels” from “sensation novels.