Advances in data analysis, data handling and business intelligence : proceedings of the 32nd Annual Conference | Gesellschaft für Klassifikation. Jahrestagung (32nd : 2008 : Helmut-Schmidt-Universität/ Universität der Bundeswehr Hamburg), Fink, Andreas, British Classification Society, Dutch/Flemish Classification Society

Advances in Data Analysis, Data Handling and Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), Helmut-Schmidt-University, Hamburg, July 16-18, 2008
Author: Andreas Fink, Berthold Lausen, Wilfried Seidel, Alfred Ultsch
Published by Springer Berlin Heidelberg
ISBN: 978-3-642-01043-9
DOI: 10.1007/978-3-642-01044-6

Table of Contents:

• Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification
• Strategies of Model Construction for the Analysis of Judgment Data
• Clustering of High-Dimensional Data via Finite Mixture Models
• Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data
• Kernel Methods for Detecting the Direction of Time Series
• Statistical Processes Under Change: Enhancing Data Quality with Pretests
• Evaluation Strategies for Learning Algorithms of Hierarchies
• Fuzzy Subspace Clustering
• Motif-Based Classification of Time Series with Bayesian Networks and SVMs
• A Novel Approach to Construct Discrete Support Vector Machine Classifiers
• Predictive Classification Trees
• Isolated Vertices in Random Intersection Graphs
• Strengths and Weaknesses of Ant Colony Clustering
• Variable Selection for Kernel Classifiers: A Feature-to-Input Space Approach
• Finite Mixture and Genetic Algorithm Segmentation in Partial Least Squares Path Modeling: Identification of Multiple Segments in Complex Path Models
• Cluster Ensemble Based on Co-occurrence Data
• Localized Logistic Regression for Categorical Influential Factors
• Clustering Association Rules with Fuzzy Concepts
• Clustering with Repulsive Prototypes
• Weakly Homoscedastic Constraints for Mixtures of t-Distributions

Includes bibliographical references and indexes