Preface	6
	Contents	14
	Part I (Semi-) Plenary Presentations	22
	Classification and Data Mining in Musicology	24
	1 Introduction	24
	2 Music,1/f-noise, fractal and chaos	24
	3 Music and entropy	25
	4 Score information and performance	27
	Acknowledgements	28
	References	31
	Bayesian Mixed Membership Models for Soft Clustering and Classi.cation	32
	1 Introduction	32
	2 Mixed membership models	35
	3 Disability types among older adults	37
	3.1 National Long Term Care Survey	37
	3.2 Applying the mixed membership model	38
	4 Classifying publications by topic	39
	4.1 Proceedings of the National Academy of Sciences	39
	4.2 Applying the mixed membership model	40
	4.3 An alternative approach with related data	43
	4.4 Choosing K to describe PNAS topics	43
	5 Summary and concluding remarks	44
	Acknowledgments	45
	References	45
	Predicting Protein Secondary Structure with Markov Models	48
	1 Introduction	48
	2 Themethod	49
	3 Improvements	50
	4 Ongoing research	53
	5 Summary	54
	References	54
	Milestones in the History of Data Visualization: A Case Study in Statistical Historiography	55
	1 Introduction	55
	1.1 The Milestones Project	56
	2 Milestones tour	57
	2.1 1600-1699: Measurement and theory	57
	2.2 1700-1799: New graphic forms	58
	2.3 1800-1850: Beginnings of modern graphics	58
	2.4 1850-1900: The Golden Age of statistical graphics	60
	2.5 1900-1950: The modern dark ages	60
	2.6 1950-1975: Re-birth of data visualization	61
	3 Problems and methods in statistical historiography	62
	3.1 What counts as a Milestone?	62
	3.2 Who gets credit?	63
	3.3 Dating milestones	63
	3.4 What is milestones “data”	64
	3.5 Analyzing milestones “data”	64
	3.6 What was he thinking?: Understanding through reproduction	64
	3.7 What kinds of tools are needed?	65
	4 How to visualize a history?	66
	4.1 Lessons from the past	67
	4.2 Lessons from the present	68
	4.3 Lessons from the web	69
	4.4 Lessons from the data visualization	70
	Acknowledgments	70
	References	71
	Quantitative Text Typology: The Impact of Word Length	74
	1 Introduction: Structuring the universe of texts	74
	1.1 Classification and quantification	74
	1.2 Quantitative text analysis: From a de.nition of the basics towards data homogeneity	75
	1.3 Word length in a synergetic context	76
	1.4 Qualitative and quantitative classi.cations: A priori and a posteriori	77
	2 A case study: Classifying 398 Slovenian texts	78
	2.1 Post hoc analysis of mean word length	80
	2.2 Discriminant analyses: The whole corpus	80
	2.3 From four to two letter types	81
	2.4 Towards a new typology	82
	2.5 Conclusion	85
	References	85
	Cluster Ensembles	86
	1 Introduction	86
	2 Consensus partitions	88
	3 Extensions	91
	References	92
	Bootstrap Confidence Intervals for Three-way Component Methods	94
	1 Introduction	94
	2 The bootstrap for fully determined solutions	95
	3 Smaller bootstrap intervals using transformations	98
	4 Performance of bootstrap confidence intervals	99
	5 An application: Bootstrap confidence intervals for results from a Tucker3 Analysis	100
	6 Discussion	102
	References	104
	Organising the Knowledge Space for Software Components	106
	1 Introduction	106
	2 The software development process	107
	3 A knowledge space for software development	109
	4 Organising the knowledge space	110
	4.1 Ontologies	110
	4.2 A discovery and composition ontology	111
	4.3 Description of components	112
	4.4 Discovery and composition of components	113
	5 Conclusions	116
	References	116
	Multimedia Pattern Recognition in Soccer Video Using Time Intervals	118
	1 Introduction	118
	2 Multimedia event classification framework	119
	2.1 Pattern representation	120
	2.2 Pattern classification	122
	3 Highlight event classification in soccer broadcasts	124
	4 Evaluation	126
	4.1 Evaluation criteria	126
	4.2 Classification results	127
	5 Conclusion	128
	References	129
	Quantitative Assessment of the Responsibility for the Disease Load in a Population	130
	1 Introduction	130
	2 Basic definitions of attributable risk	131
	3 Crude and adjusted attributable risk	132
	4 Sequential attributable risk	133
	5 Partial attributable risk	134
	6 Illustrative example: The G.R.I.P.S. Study	135
	7 Conclusion	136
	Acknowledgment	137
	References	137
	Part II Classification and Data Analysis	140
	Bootstrapping Latent Class Models	142
	1 Introduction	142
	2 Bootstrap analysis	143
	3 Bootstrap analysis in .nite mixture models	144
	4 An application to the latent class model	145
	5 Conclusion	149
	References	149
	Dimensionality of Random Subspaces	150
	1 Introduction	150
	2 Model aggregation	151
	3 Random Subspace Method	152
	4 Feature selection for ensembles	153
	5 Proposed method	154
	6 Related work	154
	7 Experiments	155
	8 Summary	156
	References	156
	Two-stage Classification with Automatic Feature Selection for an Industrial Application	158
	1 Introduction	158
	2 Two-stage classification	159
	2.1 Motivation	159
	2.2 First stage – object classification	160
	2.3 Second stage – image sequence classification	160
	2.4 Polynomial classifier	160
	3 System optimization	161
	3.1 Wrapper approach	161
	3.2 Search strategies in feature subsets	162
	3.3 Efficiency	162
	4 Experimental results	163
	5 Conclusion and outlook	165
	References	165
	Bagging, Boosting and Ordinal Classification	166
	1 Introduction	166
	2 Aggregating classi.ers	166
	3 Ordinal prediction	168
	4 Empirical studies	171
	5 Concluding remarks	172
	References	173
	A Method for Visual Cluster Validation	174
	1 Introduction	174
	2 Optimal projection for separation	176
	3 Optimal projection for heterogeneity	177
	4 Example	178
	5 Conclusion	181
	References	181
	Empirical Comparison of Boosting Algorithms	182
	1 Introduction	182
	2 Arcing algorithms	183
	2.1 Adaboost	183
	2.2 Arcing family	185
	3 Empirical study	185
	3.1 Base classi.er and performance measure	186
	3.2 Results	186
	4 Conclusion	187
	References	188
	Iterative Majorization Approach to the Distance-based Discriminant Analysis	189
	1 Introduction	189
	2 Problem formulation	190
	3 Iterative majorization	191
	4 Dimensionality reduction and multiple-class setting	193
	5 Experimental results	194
	References	196
	An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables	197
	1 Background and summary of approach	197
	2 The CHAID algorithm	198
	3 Latent class modeling	200
	4 The hybrid CHAID algorithm	201
	5 Empirical example	202
	6 Final comments	203
	References	204
	Expectation of Random Sets and the ‘Mean Values’ of Interval Data	205
	1 Introduction	205
	2 Reduction to characteristic points	206
	3.1 The Aumann expectation	207
	3.2 The Frechet expectation	207
	3.3 The Doss expectation	208
	3.4 The Vorob’ev expectation	208
	4 Expectations of Random Closed Rectangles	209
	4.1 The Aumann expectation	209
	4.2 The Frechet expectation	211
	4.3 The Doss expectation	211
	4.4 The Vorob’ev expectation	211
	5 Discussion	212
	References	212
	Experimental Design for Variable Selection in Data Bases	213
	1 Introduction	213
	2 Data	214
	3 Plackett-Burman designs	214
	4 Results	216
	4.1 Stepwise regression by forward selection	216
	4.2 Classification methods	216
	4.3 Variable assessment	217
	5 Conclusion	220
	References	220
	KMC/EDAM: A New Approach for the Visualization of K-Means Clustering Results	221
	1 Introduction	221
	2 Methods	222
	2.1 Preliminaries	222
	2.2 Basic idea	223
	2.3 KMC/EDAM	223
	3 Examples	225
	4 Conclusion	228
	References	228
	Clustering of Variables with Missing Data: Application to Preference Studies	229
	1 Introduction	229
	2 Clustering of variables around latent components	230
	3 Imputation methods	230
	3.1 Direct imputation methods	230
	3.2 Imputation within each cluster	230
	3.3 Method based on a cross-partition	231
	4 Illustration: data set ’jam’	232
	5 Simulation study	233
	5.1 Jam data set	233
	5.2 Simulated data	233
	5.3 Criterion for comparison	233
	5.4 Results	234
	6 Conclusion	236
	Acknowledgment	236
	References	236
	Binary On-line Classification Based on Temporally Integrated Information	237
	1 General framework	237
	1.1 Data format	238
	1.2 On-line classification	238
	2 Integration of information across time	239
	3 Application	240
	3.1 Neurophysiology	241
	3.2 Model	241
	3.3 Results	243
	References	244
	Different Subspace Classification	245
	1 Introduction	245
	2 Notationandmethod	246
	2.1 Characteristic regions	246
	2.2 Classification rule	247
	3 Visualization	248
	4 Parameter choice for DiSCo	249
	4.1 Building the regions	249
	4.2 Optimizing the thresholds	250
	5 Simulation study	250
	5.1 Data generation	250
	5.2 Results	251
	6 Summary	252
	References	252
	Density Estimation and Visualization for Data Containing Clusters of Unknown Structure	253
	1 Introduction	253
	2 Information optimal sets, Pareto Radius, PDE	254
	3 PDE in one dimension: PDEplot	256
	4 Measuring and visualization of density of high dimensional data	257
	5 Summary	259
	References	260
	Hierarchical Mixture Models for Nested Data Structures	261
	1 Introduction	261
	2 Model formulation	262
	2.1 Standard finite mixture model	262
	2.2 Hierarchical finite mixture model	262
	3 Maximum likelihood estimation by an adapted EM algorithm	264
	4 An empirical example	265
	5 Variants and extensions	267
	References	268
	Iterative Proportional Scaling Based on a Robust Start Estimator	269
	1 Introduction	269
	2 Covariance selection models	270
	3 Iterative proportional scaling (IPS)	271
	4 IPS robustified	272
	5 Model selection with RIPS	273
	6 Open questions	276
	References	276
	Exploring Multivariate Data Structures with Local Principal Curves	277
	1 Introduction	277
	2 Local principal curves	278
	3 Simulated data examples	281
	5 Conclusion	283
	References	283
	A Three-way Multidimensional Scaling Approach to the Analysis of Judgments About Persons	285
	1 Introduction	285
	2 The structure of judgments about persons	285
	3 ‘SUMM–ID’ model	286
	4 Application	290
	5 Concluding remarks	291
	References	292
	Discovering Temporal Knowledge in Multivariate Time Series	293
	1 Introduction	293
	2 Data	294
	3 Unification-based Temporal Grammar	294
	4 Time Series Knowledge Mining	296
	5 Discussion	298
	6 Summary	299
	Acknowledgements	299
	References	300
	A New Framework for Multidimensional Data Analysis	301
	1 Information in data	301
	2 Illustrative example	302
	3 Geometric model for categorical data	304
	4 Squared item-component correlation	304
	5 Correlation between multidimensional variables	305
	6 Decomposition of information in data and total information	306
	7 Conclusion	307
	References	308
	External Analysis of Two-mode Three-way Asymmetric Multidimensional Scaling	309
	1 Introduction	309
	2 Themethod	310
	3 An application	311
	4 Discussion	314
	References	316
	The Relevance Vector Machine Under Covariate Measurement Error	317
	1 Introduction	317
	2 Nonparametric regression using the RVM	318
	2.1 The RVM model setup	318
	2.2 Inference	319
	3 Covariate measurement error and its correction	320
	3.1 The classical error model	320
	3.2 Error correction using regression calibration	320
	3.3 Error correction using SIMEX	321
	3.4 Simulation results for the SIMEX	322
	4 Discussion	323
	Acknowledgements	324
	References	324
	Part III Applications	326
	A Contribution to the History of Seriation in Archaeology	328
	1 Introduction	328
	2 The early years	328
	3 Mathematicalmodels	329
	4 The method of Brainerd and Robinson	330
	5 Permutation search	331
	6 Towards correspondence analysis	332
	References	335
	Model-based Cluster Analysis of Roman Bricks and Tiles from Worms and Rheinzabern	338
	1 Introduction and task	338
	2 Model-based Gaussian clustering	340
	3 Results and archaeological discussion	342
	4 Conclusion	345
	References	345
	Astronomical Object Classification and Parameter Estimation with the Gaia Galactic Survey Satellite	346
	1 The Gaia Galactic survey mission	346
	2 Astrophysical data	346
	3 Classification challenges	347
	4 Outlook	348
	References	349
	Design of Astronomical Filter Systems for Stellar Classification Using Evolutionary Algorithms	351
	1 Astrophysical context	351
	2 The optimization model	352
	2.1 Parametrization	352
	2.2 Figure-of-merit (fitness)	353
	2.3 Evolutionary algorithm	354
	3 Application, results and interpretation	355
	4 Conclusions and future work	358
	References	358
	Analyzing Microarray Data with the Generative Topographic Mapping Approach	359
	1 Introduction	359
	2 Data structure	360
	3 The GTM approach	361
	4 Application to a data set	363
	5 Summary and outlook	365
	References	366
	Test for a Change Point in Bernoulli Trials with Dependence	367
	1 Introduction	367
	2 Test problem	368
	3 Intercalary independence of Markov processes	370
	4 Strategies for performing a test	371
	5 Example	372
	References	373
	Data Mining in Protein Binding Cavities	375
	1 Introduction	375
	2 Other approaches	376
	3 Theory and algorithm	377
	4 First results	379
	5 Conclusions	380
	References	381
	Classification of In Vivo Magnetic Resonance Spectra	383
	1 Introduction	383
	2 Data	384
	2.1 General features	384
	2.2 Details	384
	3 Methods	385
	3.1 Evaluated algorithms	386
	3.2 Benchmark settings	387
	4 Results	388
	5 Conclusions	390
	References	390
	Modifying Microarray Analysis Methods for Categorical Data – SAM and PAM for SNPs	391
	1 Introduction	391
	2 Multiple testing and the false discovery rate	392
	3 Significance analysis of microarrays	393
	4 SAM applied to single nucleotide polymorphisms	394
	5 Prediction analysis of microarrays	395
	6 Prediction analysis of SNPs	396
	7 Discussion	397
	References	398
	Improving the Identification of Differentially Expressed Genes in cDNA Microarray Experiments	399
	1 Introduction	399
	2 Data sets, LogRatio, RelDi.	400
	3 Comparison of LogRatio and RelDi.	401
	4 Stabilization of variance	405
	5 Summary	406
	References	406
	PhyNav: A Novel Approach to Reconstruct Large Phylogenies	407
	1 Introduction	407
	2 Minimal k-distance subsets	408
	3 The PhyNav algorithm	409
	4 The efficiency of PhyNav	409
	4.1 Simulated datasets	410
	4.2 Biological datasets	411
	5 Discussion and conclusion	412
	Acknowledgments	413
	References	413
	NewsRec, a Personal Recommendation System for News Websites	415
	1 Introduction	415
	2 Requirements, system design, and implementation details	417
	3 Website classi.cation and evaluation measures	418
	4 Empirical results	419
	5 Conclusions and outlook	420
	References	422
	Clustering of Large Document Sets with Restricted Random Walks on Usage Histories	423
	1 Motivation	423
	2 Clustering with purchase histories	424
	3 Time complexity	428
	4 Results	428
	5 Outlook	430
	References	430
	Fuzzy Two-mode Clustering vs. Collaborative Filtering	431
	1 Introduction	431
	2 Two-mode data analysis	432
	2.1 Memory-based Collaborative Filtering (CF)	432
	2.2 (Fuzzy) Two-Mode Clustering (FTMC)	433
	3 The Delta-Method for fuzzy two-mode clustering	434
	4 Examples and comparisons	435
	5 Conclusions	437
	References	437
	Web Mining and Online Visibility	439
	1 Introduction – “Why measurement of online visibility?”	439
	2 (Human) Online search in a changing webgraph	439
	2.1 The web as a graph	440
	2.2 (Human) Online searching and sur.ng behavior	441
	3 Measurement of Online Visibility	441
	3.1 Main drivers of Online Visibility	442
	3.2 Web data used for our sample	442
	3.3 The measure GOVis	443
	3.4 Results	444
	4 Conclusion and managerial implications	445
	References	446
	Analysis of Recommender System Usage by Multidimensional Scaling	447
	1 Introduction	447
	2 Methodology	448
	3 Empirical results	449
	3.1 The data sets	449
	3.2 Representation of products and search profiles	450
	3.3 Analysis of system usage	451
	4 Summary	453
	References	454
	On a Combination of Convex Risk Minimization Methods	455
	1 Introduction	455
	2 Strategy	455
	3 Kernel logistic regression and e.support vector regression	458
	4 Application	460
	5 Discussion	462
	Acknowledgments	462
	References	462
	Credit Scoring Using Global and Local Statistical Models	463
	1 Introduction	463
	2 Description of the data set	464
	3 Global scoring model	464
	3.1 Global scoring using logistic discriminant analysis	464
	3.2 Classification rule under constraints	465
	4 Local scoring by two-stage classification	466
	4.1 Clustering using self-organizing maps	467
	4.2 K-means cluster analysis	468
	4.3 Evaluation of two-stage classi.cation	468
	5 Application to the test sample	469
	6 Conclusions	470
	References	470
	Informative Patterns for Credit Scoring: Support Vector Machines Preselect Data Subsets for Linear Discriminant Analysis	471
	1 Introduction	471
	2 LinearSVMandLDA	472
	3 Subset preselection for LDA: Empirical results	475
	3.1 About typical and critical subsets	475
	3.2 LDA with subset preselection	476
	3.3 Comparing SVM, LDA and LDA-SP	476
	3.4 Advantages of LDA with subset preselection	477
	4 Conclusions	477
	References	478
	Application of Support Vector Machines in a Life Assurance Environment	479
	1 Introduction	479
	2 Support vector machines	480
	3 Problem context and the data	481
	4 A measure of variable importance	482
	5 Results	484
	References	486
	Continuous Market Risk Budgeting in Financial Institutions	487
	1 Introduction	487
	2 Analysis framework	488
	3 Time dimension of risk limits	489
	4 Continuous risk budgeting	490
	5 Simulation analysis	492
	Acknowledgement	493
	References	494
	Smooth Correlation Estimation with Application to Portfolio Credit Risk	495
	1 Introduction	495
	2 The sector variable	496
	3 Testing for independence	497
	4 Model generation	498
	5 A one-factor model	499
	6 Algebraic approximation	500
	7 Impact on the practical performance	501
	References	501
	A Appendix	502
	How Many Lexical-semantic Relations are Necessary?	503
	1 Introduction	503
	2 Concept calculus	504
	3 Diagrammatic representation	506
	4 Concept and linguistic sign	509
	5 Summary	510
	References	510
	Automated Detection of Morphemes Using Distributional Measurements	511
	1 Overview and introduction	511
	2 Why bother with the segmentation of words at all?	512
	3 The historical background of research: Distributional analysis	512
	4 Basicmethod	513
	5 Re.nements of the evaluation	515
	6 Transferring graphemic to phonemic representation	516
	7 Concluding remarks	517
	References	518
	Classification of Author and/or Genre? The Impact of Word Length	519
	1 Word length and the quantitative description of text(s) and author(s)	519
	2 A case study: text basis and analytical options	520
	3 Methods of text discrimination	521
	3.1 Quantitative measures for text analysis	522
	3.2 Discriminant analysis	523
	3.3 Statistical distance as a measure for data discrimination	523
	4 Summary	526
	References	526
	Some Historical Remarks on Library Classification – a Short Introduction to the Science of Library Classification	527
	1 Introduction	527
	2 Classified arrangement in monastery libraries of the Middle Ages	528
	3 Classified arrangement in private libraries of the Middle Ages	528
	4 Classified arrangement in the late Middle Ages and at the beginning of modern times	529
	6 Systematic cataloguing in the 18th century	530
	7 Subject cataloguing in the 19th century	530
	8 Subject cataloguing in the 20th century	531
	References	532
	Automatic Validation of Hierarchical Cluster Analysis with Application in Dialectometry	534
	1 Introduction	534
	2 Pair-wise data clustering	535
	3 Resampling techniques based on weights of observations	536
	4 Rand’s measure for comparing partitions	536
	5 A simulation study	538
	6 Application in quantitative linguistics	539
	7 Conclusions	540
	References	541
	Discovering the Senses of an Ambiguous Word by Clustering its Local Contexts	542
	1 Introduction	542
	2 Approach	543
	3 Algorithm	544
	4 Results	546
	5 Conclusions and prospects	548
	Acknowledgements	549
	References	549
	Document Management and the Development of Information Spaces	550
	1 Starting point and task	550
	2 Implementation	550
	3 Representation of the information space	551
	4 Processing .ow text	551
	5 Processing partially structured documents	554
	6 Summary and outlook	556
	References	557
	Stochastic Ranking and the Volatility “Croissant”: A Sensitivity Analysis of Economic Rankings	558
	1 Introduction	558
	2 Index definition and ranking	559
	3 Data	561
	4 Sensitivity analysis by randomised weights	562
	5 Ranking results	563
	6 Conclusions	565
	References	565
	Importance Assessment of Correlated Predictors in Business Cycles Classi.cation	566
	1 Problem	566
	1.1 Introduction	566
	1.2 Measures of importance	567
	2 Correlated predictors in regression models	567
	2.1 Overview	567
	2.2 Orthogonalization	568
	3 Correlated predictors in classi.cation models	569
	3.1 Orthogonalization	569
	3.2 Using a large number of variables	569
	3.3 Results for the business cycle model	570
	4 Discussion and outlook	571
	References	573
	Economic Freedom in the 25-Member European Union: Insights Using Classi.cation Tools	574
	1 Introduction	574
	2 Data description and distance measures	575
	2.1 Description of the economic freedom index data	575
	2.2 Distance measures	576
	3 Cluster analysis methods and cluster patterns	578
	3.1 Cluster analysis methods	578
	3.2 Empirical cluster patterns	579
	4 Conclusion and outlook	581
	References	581
	Intercultural Consumer Classifications in E-Commerce	582
	1 Introduction	582
	2 The concept of construction consumer typologies	582
	3 Characteristics for constructing typologies relevant for E-Commerce	583
	3.1 Requirements regarding criteria used for constructing typologies	583
	3.2 Selected constructs for a classi.cation	583
	4 Empirical survey of the typology theory	584
	4.1 Survey design and data collection	584
	4.2 A typology of online customers	585
	5 Conclusion	588
	References	588
	Reservation Price Estimation by Adaptive Conjoint Analysis	590
	1 Introduction	590
	2 Conjoint analysis for reservation price estimation	591
	3 Reservation price estimation based on economic theory	592
	4 Application of the method	595
	5 Conclusion and further research	596
	References	597
	Estimating Reservation Prices for Product Bundles Based on Paired Comparison Data	598
	1 Introduction	598
	2 Gathering data for conjoint measurement	599
	2.1 Direct vs. indirect elicitation of reservation prices	599
	2.2 Relative direct elicitation of reservation prices	600
	3 Study design and application situation	601
	4 Results	602
	5 Discussion	604
	References	604
	Classification of Perceived Musical Intervals	606
	1 Background	606
	2 Experimental setting	608
	3 Results	610
	4 Conclusion	612
	References	613
	In Search of Variables Distinguishing Low and High Achievers in Music Sight Reading Task	614
	1 Background	614
	2 Method	615
	3 Results	617
	4 Discussion	619
	References	620
	Automatic Feature Extraction from Large Time Series	621
	1 Introduction	621
	2 Systematization of statistical methods	622
	2.1 Windowing extends the method space	622
	2.2 Method trees for feature extraction	623
	2.3 Dynamic windowing in method trees	624
	3 Automatic feature extraction	625
	4 Experiments	626
	4.1 Results	627
	5 Conclusion	627
	References	628
	Identification of Musical Instruments by Means of the Hough-Transformation	629
	1 The Hough-transform	629
	2 Application to sound data	630
	2.1 Digital sounds	630
	2.2 Motivation: signal edges	630
	2.3 Parametrization	631
	2.4 Resulting data format	631
	3 Classification	632
	3.1 Approaches	632
	3.2 Data set	633
	3.3 Methods	633
	3.4 Variable selection	634
	3.5 Results	634
	3.6 Comparing the results	635
	4 Conclusions	636
	References	636
	Support Vector Machines for Bass and Snare Drum Recognition	637
	1 Introduction	637
	2 Previous work	638
	3 Data gathering	639
	4 Descriptors for audio	640
	5 Support Vector Machines	641
	6 Experiments and results	642
	7 Conclusions and future work	643
	Acknowledgements	644
	References	644
	Register Classification by Timbre	645
	1 Introduction	645
	2 Data	646
	3 Classification methods	647
	4 Results	648
	4.1 Individual tones, voices only	648
	4.2 Individual tones, voices and instruments	649
	4.3 Averaged tones, voices only	649
	4.4 Averaged tones, voices and instruments	649
	5 Acoustics	650
	6 Conclusion	651
	References	652
	Classification of Processes by the Lyapunov Exponent	653
	1 Introduction	653
	2 Lyapunov exponent	654
	3 Well-predictable and not-well-predictable processes	656
	4 Experimental results	658
	5 Conclusion	659
	References	659
	Desirability to Characterize Process Capability	661
	1 Introduction	661
	2 Combining capability and desirability - the indices EDU and EDM	663
	3 Discussion	665
	4 Estimation	666
	5 Simulation	667
	6 Conclusion	668
	References	668
	Application and Use of Multivariate Control Charts in a BTA Deep Hole Drilling Process	669
	1 Introduction	669
	2 Monitoring the process using multiple Residual Shewhart control charts	670
	3 Monitoring the process using multivariate control charts	671
	3.1 Data depth	671
	3.2 A control chart based on sequential rank of data depth measures	672
	4 Application	673
	4.1 Choice of the control charts parameters	673
	4.2 Results	674
	4.3 Discussion	675
	5 Conclusion	675
	Acknowledgements	676
	References	676
	Determination of Relevant Frequencies and Modeling Varying Amplitudes of Harmonic Processes	677
	1 Introduction	677
	2 Determination of the distribution of periodogram ordinates	678
	3 Regression models on periodogram ordinates	679
	3.1 Modelling varying amplitudes	679
	3.2 Estimating the variance of e (s2 e )	680
	4 Simulation study on time-varying amplitudes	680
	4.1 Design considerations	680
	4.2 Results	681
	5 Conclusions	684
	References	684
	Part IV Contest: Social Milieus in Dortmund	686
	Introduction to the Contest “Social Milieus in Dortmund”	688
	1 Contest goal and data	688
	Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering	695
	1 The problem	695
	2 Tackling the problem	695
	3 Methods	696
	3.1 Fuzzy clustering	696
	3.2 Measuring the clustering quality	697
	3.3 Defining subgroups of variables	697
	3.4 Genetic optimization algorithms	698
	3.5 Implementation	699
	4 Applying the procedure	699
	4.1 The Dortmund data	699
	4.2 Results	700
	4.3 Comparing the results	701
	5 Summary	702
	References	702
	Annealed k-Means Clustering and Decision Trees	703
	1 Introduction	703
	2 Preprocessing	704
	3 Clustering	704
	3.1 Annealed k-means	704
	3.2 Learning about k	705
	3.3 Solution	706
	4 Classification	706
	5 Interpretation	708
	6 Outlook	709
	References	710
	Correspondence Clustering of Dortmund City Districts	711
	1 Introduction	711
	2 Material and methods	712
	3 Results	715
	4 Conclusion	718
	References	718
	Keywords	719
	Authors	724