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

This is joint work with Dan Klein, Chris Manning and others. Weak Signals and Text Mining II - Text Mining Background and Application Ideas. This led me to explore probabilistic models for clustering, constrained clustering, and classification with very little labeled data, with applications to text mining. Srivastava, Ashok N., Sahami, Mehran. 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. 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). 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. Link to MnCat Record · Read about this book on Amazon Text mining : classification, clustering, and applications. Whether or not the algorithm divides a set in successive binary splits, aggregates into overlapping or non-overlapping clusters. Unsupervised methods can take a range of forms and the similarity to identify clusters. And Lafferty, J.D., “Topic Models”, Text mining: classification, clustering, and applications., 2009, pp. 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.