Batagelj / Bock / Ferligoj Data Science and Classification

1. Auflage 2006
ISBN: 978-3-540-34416-2
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 358 Seiten

Autoren/Hrsg.

Batagelj, Vladimir

Bock, Hans-Hermann

Ferligoj, Anuška

Žiberna, Aleš

Inhaltsverzeichnis

1;Preface;6
2;The 10th IFCS Conference – a Jubilee;7
3;Contents;9
4;Similarity and Dissimilarity;13
4.1;A Tree-Based Similarity for Evaluating Concept Proximities in an Ontology;14
4.2;Improved Frechet Distance for Time Series;23
4.3;Comparison of Distance Indices Between Partitions;31
4.4;Design of Dissimilarity Measures: A New Dissimilarity Between Species Distribution Areas;39
4.5;Dissimilarities for Web Usage Mining;48
4.6;Properties and Performance of Shape Similarity Measures;56
5;Classification and Clustering;66
5.1;Hierarchical Clustering for Boxplot Variables;67
5.2;Evaluation of Allocation Rules Under Some Cost Constraints;75
5.3;Crisp Partitions Induced by a Fuzzy Set;82
5.4;Empirical Comparison of a Monothetic Divisive Clustering Method with the Ward and the k- means Clustering Methods;90
5.5;Model Selection for the Binary Latent Class Model: A Monte Carlo Simulation;98
5.6;Finding Meaningful and Stable Clusters Using Local Cluster Analysis;107
5.7;Comparing Optimal Individual and Collective Assessment Procedures;115
6;Network and Graph Analysis;123
6.1;Some Open Problem Sets for Generalized Blockmodeling;124
6.2;Spectral Clustering and Multidimensional Scaling: A Unified View;136
6.3;Analyzing the Structure of U.S. Patents Network;145
6.4;Identifying and Classifying Social Groups: A Machine Learning Approach;153
7;Analysis of Symbolic Data;162
7.1;Multidimensional Scaling of Histogram Dissimilarities;163
7.2;Dependence and Interdependence Analysis for Interval- Valued Variables;173
7.3;A New Wasserstein Based Distance for the Hierarchical Clustering of Histogram Symbolic Data;186
7.4;Symbolic Clustering of Large Datasets;194
7.5;A Dynamic Clustering Method for Mixed Feature- Type Symbolic Data;203
8;General Data Analysis Methods;211
8.1;Iterated Boosting for Outlier Detection;212
8.2;Sub-species of Homopus Areolatus? Biplots and Small Class Inference with Analysis of Distance;220
8.3;Revised Boxplot Based Discretization as the Kernel of Automatic Interpretation of Classes Using Numerical Variables;228
9;Data and Web Mining;237
9.1;Comparison of Two Methods for Detecting and Correcting Systematic Error in High- throughput Screening Data;238
9.2;kNN Versus SVM in the Collaborative Filtering Framework;247
9.3;Mining Association Rules in Folksonomies;257
9.4;Empirical Analysis of Attribute-Aware Recommendation Algorithms with Variable Synthetic Data;267
9.5;Patterns of Associations in Finite Sets of Items;275
10;Analysis of Music Data;283
10.1;Generalized N-gram Measures for Melodic Similarity;284
10.2;Evaluating Different Approaches to Measuring the Similarity of Melodies;294
10.3;Using MCMC as a Stochastic Optimization Procedure for Musical Time Series;302
10.4;Local Models in Register Classification by Timbre;310
11;Gene and Microarray Analysis;318
11.1;Improving the Performance of Principal Components for Classification of Gene Expression Data Through Feature Selection;319
11.2;A New Efficient Method for Assessing Missing Nucleotides in DNA Sequences in the Framework of a Generic Evolutionary Model;327
11.3;New Efficient Algorithm for Modeling Partial and Complete Gene Transfer Scenarios;335
12;List of Reviewers;344
13;Key words;346
14;Authors;349