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Data Mining mit genetischen Algorithmen: Eine theoretische und empirische Untersuchung ausgewählter Aufgabenstellungen

Data Mining mit genetischen Algorithmen: Eine theoretische und empirische Untersuchung ausgewählter Aufgabenstellungen

          
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About the Book

Inhaltsangabe: Einleitung: Data Mining (DM) ist ein Oberbegriff für eine Reihe von Verfahren, die zur automatischen Gewinnung von Informationen aus großen Datenmengen dienen. Genetische Algorithmen (GA) sind Such- und Optimierungsverfahren, die in Analogie zur biologischen Evolution arbeiten und für DM-Aufgaben eingesetzt werden können. Dies geschieht bislang nur sporadisch und an untergeordneter Stelle. Die steigende Verfügbarkeit leistungsstarker Hardware und paralleler Systemarchitekturen machen die Benutzung von GA aber auch für die routinemäßig anfallenden Aufgaben der Datenanalyse immer interessanter. Das Ziel der Arbeit ist die Untersuchung von GA als Lösungsverfahren für typische DM-Aufgaben der Unternehmenspraxis. Dazu wird zum einen die prinzipielle Vorgehensweise beim Erfüllen von DM-Aufgaben mit GA diskutiert. Eine Analyse von DM-Aufgaben führt darüber hinaus zu einer Systematisierung in mehrere Aufgaben-Klassen, die sich hinsichtlich ihrer Relevanz für unternehmerische Fragestellungen unterscheiden. Für die wichtigsten dieser Klassen werden konkrete GA-Konfigurationen vorgeschlagen und anschließend anhand von Beispieldaten in mehreren Experimenten erprobt, um auf empirischem Wege Hinweise auf ihre Brauchbarkeit zu gewinnen. Dem potentiellen Benutzer werden die Potentiale von GA zum DM verdeutlicht und darüber hinaus Hilfestellungen gegeben, die eine praktische Anwendung dieser Methode erleichtern. Inhaltsverzeichnis: Inhaltsverzeichnis: 1.Einleitung1 1.1Motivation1 1.2Problemstellung2 1.3Gang der Untersuchung2 2.Data Mining - Grundlagen und Methoden3 2.1Formalisierungen3 2.2Begriffsabgrenzung4 2.3Einflussfaktoren für die Methodenwahl6 2.4Unterschiedliche Daten6 2.5Unterschiedliche Ziele7 2.5.1Übersicht7 2.5.2Ableitungsvorschriften8 2.5.3Verknüpfungsregeln10 2.5.4Segmentierung13 2.5.5Datenbeschreibung14 2.6Ausgewählte Verfahren des Data Mining15 2.6.1Übersicht15 2.6.2Market Basket Analysis16 2.6.3Regelinduktion18 2.6.4Case-based


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Product Details
  • ISBN-13: 9783838642390
  • Publisher: Diplom.de
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Spine Width: 7 mm
  • Weight: 154 gr
  • ISBN-10: 3838642392
  • Publisher Date: 24 Jun 2001
  • Height: 210 mm
  • No of Pages: 108
  • Series Title: German
  • Sub Title: Eine theoretische und empirische Untersuchung ausgewählter Aufgabenstellungen
  • Width: 148 mm


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