DYNAMIC FUZZY PATTERN RECOGNITION WITH APPLICATIONS TO FINANCE AND ENGINEERING LARISA ANGSTENBERGER

Transcription

1 DYNAMIC FUZZY PATTERN RECOGNITION WITH APPLICATIONS TO FINANCE AND ENGINEERING LARISA ANGSTENBERGER Kluwer Academic Publishers Boston/Dordrecht/London

2 TABLE OF CONTENTS FOREWORD ACKNOWLEDGEMENTS XIX XXI 1 INTRODUCTION GOALS AND TASKS OF THE BOOK STRUCTURE OF THE BOOK 4 2 GENERAL FRAMEWORK OF DYNAMIC PATTERN RECOGNITION THE KNOWLEDGE DISCOVERY PROCESS THE PROBLEM OF PATTERN RECOGNITION The process of pattern recognition Classification of pattern recognition methods Fuzzy pattern recognition THE PROBLEM OF DYNAMIC PATTERN RECOGNITION Mathematical description and modelling of dynamic systems Terminology of dynamic pattern recognition Goals and tasks of dynamic pattern recognition 32 3 STAGES OF THE DYNAMIC PATTERN RECOGNITION PROCESS THE MONITORING PROCESS Shewhart quality control charts Fuzzy techniques for the monitoring process Fuzzy quality control charts Reject options in fuzzy pattern recognition Parametric concept of a membership function for a dynamic classifier THE ADAPTATION PROCESS Re-learning of the classifier Incremental updating of the classifier Adaptation of the classifier Learning from statistics approach Learning with a moving time window Learning with a template set Learning with a record of usefulness 73 ' Evaluation of approaches for the adaptation of a classifier 77

3 viii Table of Contents 4 DYNAMIC FUZZY CLASSIFIER DESIGN WITH POINT- PROTOTYPE BASED CLUSTERING ALGORITHMS FORMULATION OF THE PROBLEM OF DYNAMIC CLUSTERING REQUIREMENTS FOR A CLUSTERING ALGORITHM USED FOR DYNAMIC CLUSTERING AND CLASSIFICATION DETECTION OF NEW CLUSTERS ' Criteria for the detection of new clusters Algorithm for the detection of new clusters MERGING OF CLUSTERS Criteria for merging of clusters Criteria for merging of ellipsoidal clusters Criteria and algorithm for merging spherical and ellipsoidal clusters SPLITTING OF CLUSTERS Criteria for splitting of clusters Search for a characteristic pattern in the histogram Algorithm for the detection of heterogeneous clusters to be split DETECTION OF GRADUAL CHANGES IN THE CLUSTER STRUCTURE ADAPTATION PROCEDURE UPDATING THE TEMPLATE SET OF OBJECTS Updating the template set after gradual changes in the cluster structure Updating the template set after abrupt changes in the cluster structure CLUSTER VALIDITY MEASURES FOR DYNAMIC CLASSIFIERS SUMMARY OF THE ALGORITHM FOR DYNAMIC FUZZY CLASSIFIER DESIGN AND CLASSIFICATION SIMILARITY CONCEPTS FOR DYNAMIC OBJECTS IN PATTERN RECOGNITION EXTRACTION OF CHARACTERISTIC VALUES FROM TRAJECTORIES THE SIMILARITY NOTION FOR TRAJECTORIES & Pointwise similarity measures Choice of the membership function for the definition of pointwise similarity Choice of the aggregation operator for the definition of pointwise similarity Structural similarity measures Similarity model using transformation functions Similarity measures based on wavelet decomposition Statistical measures of similarity 179

4 Table of Contents ix Smoothing of trajectories before the analysis of their temporal behaviour Similarity measures based on characteristics of trajectories EXTENSION OF FUZZY PATTERN RECOGNITION METHODS BY APPLYING SIMILARITY MEASURES FOR TRAJECTORIES APPLICATIONS OF DYNAMIC PATTERN RECOGNITION METHODS BANKCUSTOMER SEGMENTATION BASEDON CUSTOMER BEHAVIOUR Description of the credit data of bank customers Goals of bank customer analysis Parameter settings for dynamic classifier design and bank customer classification Clustering of bank customers in Group ' Y' based on the whole temporal history of 24 months and using the pointwise similarity measure Clustering of bank customers in Group 'N' based on the whole temporal history of 24 months and using the pointwise similarity measure Segmentation of bank customers in Group ' Y' based on the partial temporal history and using the pointwise similarity measure Clustering of bank customers in Group 'N' based on partial temporal history and using the pointwise similarity measure Comparison of clustering results for customers in Groups ' Y' and 'N' COMPUTER NETWORK OPTIMISATION BASED ON DYNAMIC NETWORK LOAD CLASSIFICATION Data transmission in computer networks Data acquisition and pre-processing for the network analysis Goals of the analysis of load in a computer network Parameter settings for dynamic classifier design and classification of network traffic Recognition of typical load states in a qpmputer network using the pointwise similarity measure Recognition of typical load states in computer network using the structural similarity measure CONCLUSIONS 265 REFERENCES 269

5 Table of Contents APPENDIX 279 UNSUPERVISED OPTIMAL FUZZY CLUSTERING ALGORITHM OF GATH AND GEVA 279 DESCRIPTION OF IMPLEMENTED SOFTWARE 282 INDEX, 285