Foundations of Data Mining and Knowledge Discovery

2005
ISBN: 978-3-642-43228-6
Verlag: Springer

Buch, Englisch, Band 6, 378 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 593 g

Reihe: Studies in Computational Intelligence

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.

Zielgruppe

Research

Autoren/Hrsg.

Lin, Tsau Young

Ohsuga, Setsuo

Tsumoto, Shusaku

Liau, Churn-Jung

Hu, Xiaohua

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik

Inhaltsverzeichnis

From the contents: Part I Foundations of Data Mining; Knowledge Discovery as Translation; Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities; Comparative Study of Sequential Pattern Mining Models; Designing Robust Regression Models; A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases; A Careful Look at the Use of Statistical Methodology in Data Mining; Justification and Hypothesis Selection in Data Mining.- Part II Methods of Data Mining; A Comparative Investigation on Model Selection in Binary Factor Analysis; Extraction of Generalized Rules with Automated Attribute Abstraction; Decision Making Based on Hybrid of Multi-knowledge and Naïve Bayes Classifier; First-Order Logic Based Formalism for Temporal Data Mining; An Alternative Approach to Mining Association Rules.- Part III General Knowledge Discovery; Posting Act Tagging Using Transformation-Based Learning.