Download e-book for kindle: The Text Mining Handbook by Feldman R., Sanger J.

By Feldman R., Sanger J.

ISBN-10: 0511335075

ISBN-13: 9780511335075

Textual content mining attempts to unravel the trouble of knowledge overload by way of combining ideas from info mining, computing device studying, usual language processing, details retrieval, and information administration. as well as supplying an in-depth exam of middle textual content mining and hyperlink detection algorithms and operations, this e-book examines complex pre-processing concepts, wisdom illustration issues, and visualization techniques. ultimately, it explores present real-world, mission-critical purposes of textual content mining and hyperlink detection in such diversified fields as M&A enterprise intelligence, genomics study and counter-terrorism actions.

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Extra resources for The Text Mining Handbook

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The three most common types of patterns encountered in text mining are distributions (and proportions), frequent and near frequent sets, and associations. Typically, when they offer the capability of discovering more than one type of pattern, text mining systems afford users the ability to toggle between displays of the different types of patterns for a given concept or set of concepts. This allows the richest possible exploratory access to the underlying document collection data through a browser.

Thus, for example, f (W, {iran, nicaragua, reagan} is the fraction of documents in the World Affairs collection labeled with iran, nicaragua, and reagan; f (reagan) is the proportion of the collection labeled with the concept reagan; and f (G8) is the proportion labeled with any (G8) country. By employing definitions of selection and proportion, text mining systems can already begin identifying some useful quantities for analyzing a set of documents. For example, a text mining system might want to identify the proportion of those documents labeled with K2 that are also labeled by K1 , which could be designated by expression f(D/K2 , K1 ).

Additionally, users of text mining systems with large document collections or frequent updates would have to endure more significant interruptions in their knowledge mining activities than if a quicker updating mechanism employing methods of modifying search results on an increment-by-increment basis were implemented. The more useful and sophisticated approach is to leverage knowledge from previous search runs as a foundation to which new information can be added incrementally. Several algorithms have been described for handling the incremental update situations in data mining, and these algorithms also have applicability in text mining.

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The Text Mining Handbook by Feldman R., Sanger J.

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