Text Mining: Classification, Clustering, and Applications by Ashok Srivastava, Mehran Sahami

Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami ebook
Format: pdf
ISBN: 1420059408, 9781420059403
Page: 308
Publisher: Chapman & Hall

Two basic TM tasks are classification and clustering of retrieved documents. Uncertain Spatio-temporal Applications.- Uncertain Representations and Applications in Sensor Networks.- OLAP over . Unsupervised methods can take a range of forms and the similarity to identify clusters. 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. (Genomics refers to the molecular pathways); and (c) text mining to find "non-trivial, implicit, previously unknown" patterns (p. Moreover, developers of text or literature mining applications are working at a furious pace, in part because mapping the human genome led to an explosion of text-based genetic information. 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). As a result, several large and complicated genomics and proteomics databases exist. Survey of Text Mining II: Clustering , Classification, and Retrieval . A text mining example is the classification of the subject of a document given a training set of documents with known subjects. Weak Signals and Text Mining II - Text Mining Background and Application Ideas. Etc will tend to give slightly different results. Whether or not the algorithm divides a set in successive binary splits, aggregates into overlapping or non-overlapping clusters. Issues relating to interoperability, information silos and access restrictions are limiting the uptake, degree of automation and potential application areas of text mining.