| Preface | 5 |
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| Contents | 7 |
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| Contributors | 9 |
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| Classification and Visualization | 12 |
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| Analyzing Symbolic Data: Problems, Methods, and Perspectives | 13 |
| 1 Introduction | 13 |
| 2 Visualization Tools: Zoom Stars and Principal Component Analysis | 14 |
| 3 Dissimilarity Between Data Rectangles | 15 |
| 4 Average Intervals and Class Prototypes: Centrocubes | 16 |
| 5 Partitioning Clustering Methods | 18 |
| 6 A Parametric Probabilistic Approach for Clustering Interval Data | 19 |
| 7 FinalRemarks | 21 |
| References | 21 |
| Constraining Shape and Size in Clustering | 23 |
| 1 Introduction | 23 |
| 2 Mixture Models and the EM Algorithm | 24 |
| 3 Fuzzy Clustering | 25 |
| 4 Constraining Cluster Parameters | 28 |
| 5 Experiments | 32 |
| 6 Conclusions | 34 |
| References | 35 |
| Dissolution and Isolation Robustness of Fixed Point Clusters | 36 |
| 1 Introduction | 36 |
| 2 Robustness Concepts | 37 |
| 3 Fixed Point Clusters | 39 |
| 4 Proofs | 45 |
| References | 47 |
| ADCLUS: A Data Model for the Comparison of Two- Mode Clustering Methods by Monte Carlo Simulation | 49 |
| 1 Introduction | 49 |
| 2 The ADCLUS Model as a General Model for Generating Clustered Data | 50 |
| 3 An Exemplifying Simulation Study | 54 |
| 4 Conclusions | 57 |
| References | 58 |
| Density-Based Multidimensional Scaling | 60 |
| 1 Introduction | 60 |
| 2 Sammon's Mapping | 61 |
| 3 Density-Based Mappings | 62 |
| 4 Results | 65 |
| 5 Conclusions | 67 |
| References | 67 |
| Classification of Binary Data Using a Spherical Distribution | 68 |
| 1 Introduction | 68 |
| 2 Transformation Binary Data into Directional Data | 69 |
| 3 Distribution on a Hypersphere | 71 |
| 4 Discriminant Function | 73 |
| 5 Numerical Experiments | 74 |
| 6 Concluding Remarks | 75 |
| References | 76 |
| Fuzzy Clustering Based Regression with Attribute Weights | 77 |
| 1 Introduction | 77 |
| 2 Fuzzy Clustering Considering Attributes | 78 |
| 3 Fuzzy Cluster Loading Model | 79 |
| 4 A Weighted Regression Analysis Using Fuzzy Clustering | 81 |
| 5 Attribute Based Fuzzy Cluster Loading Model and a Fuzzy Weighted Regression Analysis | 81 |
| 6 Numerical Example | 82 |
| 7 Conclusion | 85 |
| References | 85 |
| Polynomial Regression on a Dependent Variable with Immeasurable Observations | 87 |
| 1 Introduction | 87 |
| 2 Preliminaries | 88 |
| 3 Likelihood Functions | 89 |
| 4 Simulation Studies | 90 |
| 5 Conclusion | 93 |
| References | 94 |
| Methods in Fields | 95 |
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| Feedback Options for a Personal News Recommendation Tool | 96 |
| 1 Introduction | 96 |
| 2 Interest Profiles | 97 |
| 3 Feedback Options | 98 |
| 4 Web Page Classification | 99 |
| 5 Evaluation Method | 100 |
| 6 Empirical Results | 101 |
| 7 Conclusions | 102 |
| References | 103 |
| Classification in Marketing Science | 104 |
| 1 I
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