Contrast Data Mining Concepts, Algorithms, and Applications
191,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Gebundene Ausgabe
Erscheinungsdatum
07.09.2012
Abbildungen
schwarz-weiss Illustrationen, Tabellen, schwarz-weiss
Herausgeber
Dong Guozhu + weitereVerlag
Taylor and FrancisSeitenzahl
434
Maße (L/B/H)
24/16,1/2,8 cm
Gewicht
960 g
Sprache
Englisch
ISBN
978-1-4398-5432-7
"Preface Contrasting is one of the most basic types of analysis. Contrasting based analysis is routinely employed, often subconsciously, by all types of people. People use contrasting to better understand the world around them and the challenging problems they want to solve. People use contrasting to accurately assess the desirability of important situations, and to help them better avoid potentially harmful situations and embrace potentially beneficial ones. Contrasting involves the comparison of one dataset against another. The datasets may represent data of different time periods, spatial locations, or classes, or they may represent data satisfying different conditions. Contrasting is often employed to compare cases with a desirable outcome against cases with an undesirable one, for example comparing the benign and diseased tissue classes of a cancer, or comparing students who graduate with university degrees against those who do not. Contrasting can identify patterns that capture changes and trends over time or space, or identify discriminative patterns that capture differences among contrasting classes or conditions. Traditional methods for contrasting multiple datasets were often very simple so that they could be performed by hand. For example, onecould compare the respective feature means, compare the respective attribute-value distributions, or compare the respective probabilities of simple patterns, in the datasets being contrasted. However, the simplicity of such approaches has limitations, asit is difficult to use them to identify specific patterns that offer novel and actionable insights, and identify desirable sets of discriminative patterns for building accurate and explainable classifiers"--
Kundinnen und Kunden meinen
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kund*innen durch Ihre Meinung
Kurze Frage zu unserer Seite
Vielen Dank für Ihr Feedback
Wir nutzen Ihr Feedback, um unsere Produktseiten zu verbessern. Bitte haben Sie Verständnis, dass wir Ihnen keine Rückmeldung geben können. Falls Sie Kontakt mit uns aufnehmen möchten, können Sie sich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice