data mining theory methodology (6 Ergebnisse)

Zustand: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9783540325475.

Softcover/Paperback. Zustand: Sehr gut. 348 p. Very good. Shrink wrapped. / Sehr guter Zustand. In Folie verschweißt. Sprache: Englisch Gewicht in Gramm: 630.

16 x 24 cm. 344 pages. Hardcover Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 331 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Gebunden. Zustand: New. InhaltsverzeichnisIntroduction to Data Mining Decision Trees Clustering Techniques Ensemble Methods Decomposition Methodology in Data Mining Feature Set Decomposition Space Decomposition Sample Decomposition Function Decompositio.

Buch. Zustand: Neu. Neuware - Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery from Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools, and then joining them together to solve the initial problem. The benefits of decomposition methodology in data mining include: increased performance (classification accuracy), conceptual simplification of the problem, enhanced feasibility with huge databases, clearer and more comprehensible results, reduced runtime by solving smaller problems and by using parallel/distributed computation, and the opportunity of using different techniques for individual sub-problems.