Text Mining: Classification, Clustering, and Applications book
Par martin jerlene le mardi, avril 12 2016, 21:36 - Lien permanent
Text Mining: Classification, Clustering, and Applications by Ashok Srivastava, Mehran Sahami
Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami ebook
Publisher: Chapman & Hall
Format: pdf
ISBN: 1420059408, 9781420059403
Page: 308
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. 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. (Genomics refers to the molecular pathways); and (c) text mining to find "non-trivial, implicit, previously unknown" patterns (p. And Lafferty, J.D., “Topic Models”, Text mining: classification, clustering, and applications., 2009, pp. Two basic TM tasks are classification and clustering of retrieved documents. As a result, several large and complicated genomics and proteomics databases exist. Unsupervised methods can take a range of forms and the similarity to identify clusters.