Advances in data analysis, data handling and business intelligence : proceedings of the 32nd Annual Conference
Author: Gesellschaft für Klassifikation. Jahrestagung (32nd : 2008 : Helmut-Schmidt-Universität/ Universität der Bundeswehr Hamburg), Fink, Andreas, British Classification Society, Dutch/Flemish Classification Society
Added by: sketch
Added Date: 2016-01-12
Language: eng
Subjects: Statistics, Classification, Information storage and retrieval systems
Publishers: Heidelberg ; New York : Springer
Collections: journals contributions, journals
ISBN Number: 9783642010439, 3642010431
Pages Count: 300
PPI Count: 300
PDF Count: 1
Total Size: 309.51 MB
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Last Modified: 2023-05-26 03:15:10
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Description
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