Foundations of Data Mining and knowledge Discovery

• Corporate Author: SpringerLink (Online service)
• Other Authors: Young Lin, Tsau (Editor), Ohsuga, Setsuo (Editor), Liau, Churn-Jung (Editor), Hu, Xiaohua (Editor), Tsumoto, Shusaku (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
• Series: Studies in Computational Intelligence, 6
• Subjects: Computer science.; Computers.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Computer Science.; Theory of Computation.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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...

Table of Contents:

• 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.