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Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami
Publisher: Chapman & Hall

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. But they're not random: errors cluster in certain words and periods. 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. Etc will tend to give slightly different results. Weak Signals and Text Mining II - Text Mining Background and Application Ideas. A text mining example is the classification of the subject of a document given a training set of documents with known subjects. Text Mining: Classification, Clustering, and Applications. Download Text Mining: Classification, Clustering, and Applications text mining is needed when “words are not enough.†This book:. Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami. Unsupervised methods can take a range of forms and the similarity to identify clusters. Whether or not the algorithm divides a set in successive binary splits, aggregates into overlapping or non-overlapping clusters. 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. 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). Text Mining: Classification, Clustering, and Applications book download.