Text Mining: Classification, Clustering, and Applications book download
Par perry dorothy le mercredi, janvier 20 2016, 03:57 - Lien permanent
Text Mining: Classification, Clustering, and Applications by Ashok Srivastava, Mehran Sahami
Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami ebook
Page: 308
Publisher: Chapman & Hall
ISBN: 1420059408, 9781420059403
Format: pdf
Text Mining: Classification, Clustering, and Applications. Link to MnCat Record · Read about this book on Amazon Text mining : classification, clustering, and applications. This technique usually consists of finite steps, such as parsing a text into separate words, finding terms and reducing them to their basics ("truncation") followed by analytical procedures such as clustering and classification to derive patterns within the structured data, and finally evaluation and interpretation of the output. Computational pattern discovery and classification based on data clustering plays an important role in these applications. Text mining is a process including automatic classification, clustering (similar but distinct from classification), indexing and searching, entity extraction (names, places, organization, dates, etc.), statistically Practical text mining with Perl. 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. 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. Srivastava, Ashok N., Sahami, Mehran. � Of all the books listed here, this one includes the most Perl programming examples, and it is not as scholarly as the balance of the list. We consider there to be three relevant applications of our text-mining procedures in the near future:. Wiley series on methods and applications in data mining. This is joint work with Dan Klein, Chris Manning and others. This led me to explore probabilistic models for clustering, constrained clustering, and classification with very little labeled data, with applications to text mining. Text Mining and its Applications to Intelligence, CRM and Knowledge Management (Advances in Management Information) - Alessandro Zanasi (Editor), WIT Press, 2007.