Okada / Gaul / Imaizumi Cooperation in Classification and Data Analysis

Proceedings of Two German-Japanese Workshops
1. Auflage 2009
ISBN: 978-3-642-00668-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 208 Seiten

Reihe: Studies in Classification, Data Analysis, and Knowledge Organization

This volume presents theories, models, algorithms, and applications in clustering, classification, and visualization. It also includes applications of clustering, classification, and visualization in various fields such as marketing, recommendation system, biology, sociology, and social survey. The contributions give insight into new models and concepts and show the variety of research in clustering, classification, and visualization.

Autoren/Hrsg.

Gaul, Wolfgang

Bock, Hans-Hermann

Imaizumi, Tadashi

Okada, Akinori

Gaul, Wolfgang A.

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;Contributors;9
4;Classification and Visualization;12
4.1;Analyzing Symbolic Data: Problems, Methods, and Perspectives;13
4.1.1;1 Introduction;13
4.1.2;2 Visualization Tools: Zoom Stars and Principal Component Analysis;14
4.1.3;3 Dissimilarity Between Data Rectangles;15
4.1.4;4 Average Intervals and Class Prototypes: Centrocubes;16
4.1.5;5 Partitioning Clustering Methods;18
4.1.6;6 A Parametric Probabilistic Approach for Clustering Interval Data;19
4.1.7;7 FinalRemarks;21
4.1.8;References;21
4.2;Constraining Shape and Size in Clustering;23
4.2.1;1 Introduction;23
4.2.2;2 Mixture Models and the EM Algorithm;24
4.2.3;3 Fuzzy Clustering;25
4.2.4;4 Constraining Cluster Parameters;28
4.2.5;5 Experiments;32
4.2.6;6 Conclusions;34
4.2.7;References;35
4.3;Dissolution and Isolation Robustness of Fixed Point Clusters;36
4.3.1;1 Introduction;36
4.3.2;2 Robustness Concepts;37
4.3.3;3 Fixed Point Clusters;39
4.3.4;4 Proofs;45
4.3.5;References;47
4.4;ADCLUS: A Data Model for the Comparison of Two- Mode Clustering Methods by Monte Carlo Simulation;49
4.4.1;1 Introduction;49
4.4.2;2 The ADCLUS Model as a General Model for Generating Clustered Data;50
4.4.3;3 An Exemplifying Simulation Study;54
4.4.4;4 Conclusions;57
4.4.5;References;58
4.5;Density-Based Multidimensional Scaling;60
4.5.1;1 Introduction;60
4.5.2;2 Sammon's Mapping;61
4.5.3;3 Density-Based Mappings;62
4.5.4;4 Results;65
4.5.5;5 Conclusions;67
4.5.6;References;67
4.6;Classification of Binary Data Using a Spherical Distribution;68
4.6.1;1 Introduction;68
4.6.2;2 Transformation Binary Data into Directional Data;69
4.6.3;3 Distribution on a Hypersphere;71
4.6.4;4 Discriminant Function;73
4.6.5;5 Numerical Experiments;74
4.6.6;6 Concluding Remarks;75
4.6.7;References;76
4.7;Fuzzy Clustering Based Regression with Attribute Weights;77
4.7.1;1 Introduction;77
4.7.2;2 Fuzzy Clustering Considering Attributes;78
4.7.3;3 Fuzzy Cluster Loading Model;79
4.7.4;4 A Weighted Regression Analysis Using Fuzzy Clustering;81
4.7.5;5 Attribute Based Fuzzy Cluster Loading Model and a Fuzzy Weighted Regression Analysis;81
4.7.6;6 Numerical Example;82
4.7.7;7 Conclusion;85
4.7.8;References;85
4.8;Polynomial Regression on a Dependent Variable with Immeasurable Observations;87
4.8.1;1 Introduction;87
4.8.2;2 Preliminaries;88
4.8.3;3 Likelihood Functions;89
4.8.4;4 Simulation Studies;90
4.8.5;5 Conclusion;93
4.8.6;References;94
5;Methods in Fields;95
5.1;Feedback Options for a Personal News Recommendation Tool;96
5.1.1;1 Introduction;96
5.1.2;2 Interest Profiles;97
5.1.3;3 Feedback Options;98
5.1.4;4 Web Page Classification;99
5.1.5;5 Evaluation Method;100
5.1.6;6 Empirical Results;101
5.1.7;7 Conclusions;102
5.1.8;References;103
5.2;Classification in Marketing Science;104
5.2.1;1 Introduction;104
5.2.2;2 Data Description and Preprocessing;105
5.2.3;3 Growing Bisecting k-Means: A Methodological Outline;106
5.2.4;4 Application to Marketing Text Corpus;108
5.2.5;5 Conclusions;110
5.2.6;References;110
5.3;Deriving a Statistical Model for the Prediction of Spiralling in BTA Deep- Hole- Drilling from a Physical Model;112
5.3.1;1 Introduction;112
5.3.2;2 Physical Model;114
5.3.3;3 Statistical Model;115
5.3.4;4 Summary and Outlook;118
5.3.5;References;119
5.4;Analyzing Protein-Protein Interaction with Variant Analysis;120
5.4.1;1 Introduction;120
5.4.2;2 Parameter Estimation Under Ambiguity and Contamination;121
5.4.3;3 Study of a Protein-Protein Interaction;124
5.4.4;References;127
5.5;Estimation for the Parameters in Geostatistics;128
5.5.1;1 Introduction;128
5.5.2;2 Using Geostatistics for Prediction;129
5.5.3;3 Non-negative Least Squares Method for Nonlinear Model ( NNLS);132
5.5.4;4 Numerical Example;134
5.5.5;5 Conclusion and Future Research;135
5.6;Identifying Patients at Risk: Mining Dialysis Treatment Data;136
5.6.1;1 Introduction;136
5.6.2;2 Background and RelatedWork;137
5.6.3;3 The Data-set;138
5.6.4;4 Standard Model;139
5.6.5;5 Temporal Model;140
5.6.6;6 MixedModel;143
5.6.7;7 Summary and Outlook;144
5.6.8;References;144
5.7;Sequential Multiple Comparison Procedure for Finding a Changing Point in Dose Finding Test;146
5.7.1;1 Introduction;146
5.7.2;2 Sequential Multiple Comparison Procedure;147
5.7.3;3 Critical Value;148
5.7.4;4 Power of Test and Necessary Sample Size;149
5.7.5;5 A Simulation Study;150
5.7.6;6 A Case Study;151
5.7.7;7 Conclusions;152
5.7.8;References;154
5.8;Semi-supervised Clustering of Yeast Gene Expression Data;155
5.8.1;1 Introduction;155
5.8.2;2 Methods;156
5.8.3;3 Results;159
5.8.4;4 Conclusion;161
5.8.5;References;162
5.9;Event Detection in Environmental Scanning: News from a Hospitality Industry Newsletter;164
5.9.1;1 Introduction;164
5.9.2;2 Data Description and Preprocessing;165
5.9.3;3 Methodology;166
5.9.4;4 Discussion and Conclusions;171
5.9.5;References;171
6;Applications in Clustering and Visualization;172
6.1;External Asymmetric Multidimensional Scaling Study of Regional Closeness in Marriage Among Japanese Prefectures;173
6.1.1;1 Introduction;173
6.1.2;2 TheData;174
6.1.3;3 TheMethod;174
6.1.4;4 The Analysis;175
6.1.5;5 Results;175
6.1.6;6 Discussion;177
6.1.7;References;180
6.2;Socioeconomic and Age Differences in Women's Cultural Consumption: Multidimensional Preference Analysis;181
6.2.1;1 Social Patterning of Cultural Consumption;181
6.2.2;2 Data;182
6.2.3;3 Results;183
6.2.4;4 Discussion and Conclusion;188
6.2.5;References;188
6.3;Analysis of Purchase Intentions at a Department Store by Three- Way Distance Model;190
6.3.1;1 Introduction;190
6.3.2;2 Data;191
6.3.3;3 Analysis;192
6.3.4;4 Results;193
6.3.5;5 Discussion;195
6.3.6;References;197
6.4;Facet Analysis of the AsiaBarometer Survey: Well- being, Trust and Political Attitudes;198
6.4.1;1 Introduction;198
6.4.2;2 Results of Data Analysis;199
6.4.3;3 Conclusion;204
6.4.4;References;205
6.5;Author Index;206
6.6;Subject Index;207