Knowledge discovery for business information systems:
Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless,...
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Kluwer
2001
|
| Schriftenreihe: | The Kluwer international series in engineering and computer science
600 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless, our ability to discover critical, non-obvious nuggets of useful information in data that could influence or help in the decision making process, is still limited. Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing. To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis This volume contains a collection of 16 articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field focusing on the process of information discovery from large volumes of data. The field combines such areas as database concepts and theory, machine learning, pattern recognition, and artificial intelligence |
| Beschreibung: | XVII, 431 S. |
| ISBN: | 0792372433 |
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| 520 | |a Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless, our ability to discover critical, non-obvious nuggets of useful information in data that could influence or help in the decision making process, is still limited. Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing. To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis | ||
| 520 | |a This volume contains a collection of 16 articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA | ||
| 520 | |a Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field focusing on the process of information discovery from large volumes of data. The field combines such areas as database concepts and theory, machine learning, pattern recognition, and artificial intelligence | ||
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-010226426 | ||
Datensatz im Suchindex
| _version_ | 1804129860007755776 |
|---|---|
| adam_text | Contents
PREFACE xi
FOREWORD xiii
LIST OF CONTRIBUTORS xv
Chapter 1 INFORMATION FILTERS SUPPLYING DATA
WAREHOUSES WITH BENCHMARKING
INFORMATION 1
WitoldAbramowicz, PawelJan Kalczynski, KrzysztofWecel
1. Introduction 1
2. Data Warehouses 2
3. The HyperSDI System 4
4. User Profiles in the HyperSDI System 11
5. Building Data Warehouse Profiles 11
6. Techniques for Improving Profiles 18
7. Implementation Notes 22
8. Conclusions 25
References
Chapter 2 PARALLEL MINING OF ASSOCIATION RULES 29
David Cheung, Sou Dan Lee
1. Introduction 29
2. Parallel Mining of Association Rules 32
3. Pruning Techniques and The FPM Algorithm 33
4. Metrics for Data Skewness and Workload Balance 39
5. Partitioning of the Database 48
6. Experimental Evaluation of the Partitioning Algorithms 56
7. Discussions 62
8. Conclusions 64
References 65
vi CONTENTS
Chapter 3 UNSUPERVISED FEATURE RANKING
AND SELECTION 67
Manoranjan Dash, Huan Liu, Jun Yao
1. Introduction 67
2. Basic Concepts and Possible Approaches 69
3. An Entropy Measure for Continuous and Nominal Data Types 72
4. Algorithm to Find Important Variables 75
5. Experimental Studies 76
6. Clustering Using SUD 80
7. Discussion and Conclusion 82
References 84
Chapter 4 APPROACHES TO CONCEPT BASED EXPLORATION
OF INFORMATION RESOURCES 89
Hele Mai Haav, Jergen Fischer Nilsson
1. Introduction 89
2. Conceptual Taxonomies 91
3. Ontology Driven Concept Retrieval 99
4. Search based on formal concept analysis 104
5. Conclusion 109
Acknowledgements 109
References 109
Chapter 5 HYBRID METHODOLOGY OF KNOWLEDGE
DISCOVERY FOR BUSINESS INFORMATION 111
Zdzislaw S. Hippe
1. Introduction 111
2. Present Status of Data Mining 113
3. Experiments with Mining Regularities from Data 118
4. Discussion 125
Acknowledgements 126
References 126
Chapter 6 FUZZY LINGUISTIC SUMMARIES OF DATABASES
FOR AN EFFICIENT BUSINESS DATA ANALYSIS
AND DECISION SUPPORT 129
Janusz Kacprzyk, Ronald R. Yager and Siawomir Zadrozny
1. Introduction 129
2. Idea of Linguistic Summaries Using Fuzzy Logic with
Linguistic Quantifiers 131
3. On Other Validity Criteria 134
4. Derivation of Linguistic Summaries via a Fuzzy Logic
Based Database Querying Interface 140
5. Implementation for a Sales Database at a Computer Retailer 147
CONTENTS vjj
6. Concluding Remarks 150
References 150
Chapter 7 INTEGRATING DATA SOURCES USING
A STANDARDIZED GLOBAL DICTIONARY 153
Ramon Lawrence and Ken Barker
1. Introduction 154
2. Data Semantics and the Integration Problem 154
3. Previous work 156
4. The Integration Architecture 157
5. The Global Dictionary 160
6. The Relational Integration Model 164
7. Special Cases of Integration 169
8. Applications to the WWW 171
9. Future Work and Conclusions 171
References 172
Chapter 8 MAINTENANCE OF DISCOVERED ASSOCIATION
RULES 173
Sau Dan Lee, David Cheung
1. Introduction 173
2. Problem Description 176
3. The FUP Algorithm for the Insertion Only Case 179
4. The FUP Algorithm for the Deletions Only Case 183
5. The FUP2 Algorithm for the General Case 189
6. Performance Studies 194
7. Discussions 204
8. Conclusions 208
Notes 209
References 209
Chapter 9 MULTIDIMENSIONAL BUSINESS PROCESS
ANALYSIS WITH THE PROCESS WAREHOUSE 211
Beate List, Josef Schiefer, A Min Tjoa, Gerald Quirchmayr
1. Introduction 211
2. Related Work 213
3. Goals of the Data Warehouse Approach 215
4. Data Source 216
5. Basic Process Warehouse Components Representing
Business Process Analysis Requirements 216
6. Data Model and Analysis Capabilities 219
7. Conclusion and Further Research 225
References 225
viii CONTENTS
Chapter 10 AMALGAMATION OF STATISTICS AND DATA
MINING TECHNIQUES: EXPLORATIONS IN
CUSTOMER LIFETIME VALUE MODELING 229
D. R. Marti, James Drew, Andrew Betz andPiew Datta
1. Introduction 229
2. Statistics and Data Mining Techniques: A Characterization 231
3. Lifetime Value (LTV) Modeling 232
4. Customer Data for LTV Tenure Prediction 234
5. Classical Statistical Approaches to Survival Analysis 235
6. Neural Networks for Survival Analysis 239
7. From Data Models to Business Insight 244
8. Conclusion: The Amalgamation of Statistical and Data
Mining Techniques 247
References 249
Chapter 11 ROBUST BUSINESS INTELLIGENCE SOLUTIONS 251
Jan Mrazek
1. Introduction 251
2. Business Intelligence Architecture 252
3. Data Transformation 258
4. Data Modelling 260
5. Integration Of Data Mining 268
6. Conclusion 272
References 273
Chapter 12 THE ROLE OF GRANULAR INFORMATION
IN KNOWLEDGE DISCOVERY IN DATABASES 275
Witold Pedrycz
1. Introduction 276
2. Granulation of information 277
3. The development of data justifiable information granules 284
4. Building associations in databases 287
5. From associations to rules in databases 292
6. The construction of rules in data mining 293
7. Properties of rules induced by associations 298
8. Detailed computations of the consistency of rules and its
analysis 300
9. Conclusions 303
Acknowledgment 304
References 304
CONTENTS ix
Chapter 13 DEALING WITH DIMENSIONS IN DATA
WAREHOUSING 307
Jaroslav Pokomy
1. Introduction 308
2. DW Modelling with Tables 310
3. Dimensions 311
4. Constellations 314
5. Dimension Hierarchies with ISA hierarchies 315
6. Conclusions 323
References 324
Chapter 14 ENHANCING THE KDD PROCESS IN THE
RELATIONAL DATABASE MINING FRAMEWORK BY
QUANTITATIVE EVALUATION OF ASSOCIATION
RULES 325
Giuseppe Psaila
1. Introduction 325
2. The Relational Database Mining Framework 327
3. The Evaluate Rule Operator 331
4. Enhancing the Knowledge Discovery Process 344
5. Conclusions and Future Work 348
Notes 349
References 349
Chapter 15 SPEEDING UP HYPOTHESIS DEVELOPMENT 351
JorgA. Schlosser, Peter C. Lockemann, Matthias Gimbel
1. Introduction 352
2. Information Model 354
3. The Execution Architecture of CITRUS 356
4. Searching the Information Directory 359
5. Documentation of the Process History 361
6. Linking the Information Model with the Relational Model 362
7. Generation of SQL Queries 364
8. Automatic Materialization of Intermediate Results 368
9. Experimental Results 369
10. Utilizing Past Experience 3 71
11. Related Work 372
12. Concluding Remarks 3 74
References 374
X CONTENTS
Chapter 16 SEQUENCE MINING IN DYNAMIC
AND INTERACTIVE ENVIRONMENTS 377
Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara,
Sandhya Dwarkadas
1. Introduction 378
2. Problem Formulation 379
3. The SPADE Algorithm 382
4. Incremental Mining Algorithm 384
5. Interactive Sequence Mining 388
6. Experimental Evaluation 390
7. Related Work 394
8. Conclusions 395
Acknowledgements 395
References 395
Chapter 17 INVESTIGATION OF ARTIFICIAL NEURAL
NETWORKS FOR CLASSIFYING LEVELS
OF FINANCIAL DISTRESS OF FIRMS: THE CASE
OF AN UNBALANCED TRAINING SAMPLE 397
JozefZurada, Benjamin P. Foster, Terry J. Ward
1. Introduction 398
2. Motivation and Literature Review 399
3. Logit Regression, Neural Network, and Principal
Component Analysis Fundamentals 402
4. Research Methodology 409
5. Discussion of the Results 415
6. Conclusions and Future Research Directions 420
Appendix Neural Network Toolbox 421
References 423
INDEX 425
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| isbn | 0792372433 |
| language | English |
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| spelling | Knowledge discovery for business information systems ed. by Witold Abramowicz ... Dordrecht Kluwer 2001 XVII, 431 S. txt rdacontent n rdamedia nc rdacarrier The Kluwer international series in engineering and computer science 600 Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless, our ability to discover critical, non-obvious nuggets of useful information in data that could influence or help in the decision making process, is still limited. Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing. To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis This volume contains a collection of 16 articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field focusing on the process of information discovery from large volumes of data. The field combines such areas as database concepts and theory, machine learning, pattern recognition, and artificial intelligence Data mining Data warehouse Databaser Datastrukturer Abramowicz, Witold 1954- Sonstige (DE-588)141068558 oth The Kluwer international series in engineering and computer science 600 (DE-604)BV023545171 600 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010226426&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Knowledge discovery for business information systems The Kluwer international series in engineering and computer science Data mining Data warehouse Databaser Datastrukturer |
| title | Knowledge discovery for business information systems |
| title_auth | Knowledge discovery for business information systems |
| title_exact_search | Knowledge discovery for business information systems |
| title_full | Knowledge discovery for business information systems ed. by Witold Abramowicz ... |
| title_fullStr | Knowledge discovery for business information systems ed. by Witold Abramowicz ... |
| title_full_unstemmed | Knowledge discovery for business information systems ed. by Witold Abramowicz ... |
| title_short | Knowledge discovery for business information systems |
| title_sort | knowledge discovery for business information systems |
| topic | Data mining Data warehouse Databaser Datastrukturer |
| topic_facet | Data mining Data warehouse Databaser Datastrukturer |
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