Data Mining: Ein Überblick über Verfahren und Anwendungsfelder
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Inhaltsangabe: Einleitung: Die Arbeit beschäftigt sich mit der Beschreibung der gängigen Data-Mining-Verfahren und beschreibt deren Anwendungsgebiete in der Praxis. Nachdem in Kapitel 2 der Begriff Data Mining" definiert und zu verwandten Themengebieten abgegrenzt wird, werden im Schwerpunkt der Arbeit, dem dritten Kapitel, die Data-Mining-Verfahren dargestellt. Dabei werden in Kapitel 3.1 die klassischen Verfahren der Clusteranalyse beschrieben, in Kapitel 3.2 die Bayes-Klassifikation und die Assoziationsanalyse als statistische Verfahren vorgestellt und im Kapitel 3.3 eine Alternative zu den klassischen Clustermethoden vorgeführt, das konzeptionelle Clustern. Au erdem werden die Entscheidungsbaummethoden dargestellt. Das Kapitel schlie t mit einer Beschreibung von künstlichen Neuronalen Netzen und Genetischen Algorithmen ab. Im vierten Kapitel sollen dann beispielhaft praxisrelevante Anwendungsfelder beschrieben werden. Neben der Betrugserkennung, auf die bereits in der Einleitung hingedeutet wurde, soll auf die Möglichkeiten der Warenkorbanalyse, Kundensegmentierung und Datenreinigung eingegangen werden. Das Kapitel endet mit dem Versuch, durch Anwendung von Neuronalen Netzen Aktienkurse vorherzusagen. Nach einer Zusammenfassung in Kapitel 5 soll ein Ausblick gegeben werden, welche Entwicklungslinien für Data Mining" denkbar sind und welche strategische Bedeutung sich hieraus für ein Unternehmen ergibt. Inhaltsverzeichnis: Inhaltsverzeichnis: 1.Einleitung6 1.1Motivation zur Anwendung von Data Mining"6 1.2Zielsetzung der Arbeit7 1.3Aufbau und Schwerpunktsetzung8 2.Einordnung und Begriffsbestimmung8 2.1Der Gesamtprozess Knowledge Discovery in Databases (KDD)"8 2.2Definition Data Mining"10 2.3Abgrenzung zu anderen Disziplinen12 2.3.1Data Warehouse12 2.3.2Visualisierungstechniken13 2.3.3Statistik14 2.3.4Maschinelles Lernen15 2.3.5Expertensysteme16 3.Eigenschaften von Data-Mining-Verfahren17 3.1Clusteranalyse18 3.1.1Hierarchische Clusterung18
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