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 e
- PDF / 31,366,821 Bytes
- 442 Pages / 468 x 756 pts Page_size
- 80 Downloads / 324 Views
The Kluwer International Series in Engineering and Computer Science
Knowledge Discovery for Business Information Systems Edited by
Witold Abramowicz The University of Economics, Poland Jozef Zurada University of Louisville, U.S.A.
KLUWER ACADEMIC PUBLISHERS NEW YORK, BOSTON, DORDRECHT, LONDON, MOSCOW
eBook ISBN: Print ISBN:
0-306--46991-X 0-7923-7243-3
©2002 Kluwer Academic Publishers New York, Boston, Dordrecht, London, Moscow
All rights reserved
No part of this eBook may be reproduced or transmitted in any form or by any means, electronic, mechanical, recording, or otherwise, without written consent from the Publisher
Created in the United States of America
Visit Kluwer Online at: and Kluwer's eBookstore at:
http://www.kluweronline.com http://www.ebooks.kluweronline.com
Contents
PREFACE
xi
FOREWORD LIST OF CONTRIBUTORS Chapter 1
Chapter 2
INFORMATION FILTERS SUPPLYING DATA WAREHOUSES WITH BENCHMARKING INFORMATION Witold Abramowicz, 1. Introduction 2. Data Warehouses 3. The HyperSDI System 4. User Profiles in the HyperSDI System 5. Building Data Warehouse Profiles 6. Techniques for Improving Profiles 7. Implementation Notes 8. Conclusions References PARALLEL MINING OF ASSOCIATION RULES David Cheung, Sau Dan Lee 1. Introduction 2. Parallel Mining of Association Rules 3. Pruning Techniques and The FPM Algorithm 4. Metrics for Data Skewness and Workload Balance 5. Partitioning of the Database 6. Experimental Evaluation of the Partitioning Algorithms 7. Discussions 8. Conclusions References
xiii xv
1 1 2 4 11 11 18 22 25
29
29
32 33 39 48 56 62 64 65
CONTENTS
vi 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 84 References
Chapter 4
APPROACHES TO CONCEPT BASED EXPLORATION OF INFORMATION RESOURCES Hele-Mai Haav, Jørgen Fischer Nilsson 1. Introduction 2. Conceptual Taxonomies 3. Ontology Driven Concept Retrieval 4. Search based on formal concept analysis 5. Conclusion Acknowledgements References
89 91 99 104 109 109 109
HYBRID METHODOLOGY OF KNOWLEDGE DISCOVERY FOR BUSINESS INFORMATION
111
Chapter 5
5. Hippe
1. Introduction 2. Present Status of Data Mining 3. Experiments with Mining Regularities from Data 4. Discussion Acknowledgements References Chapter 6
FUZZY LINGUISTIC SUMMARIES OF DATABASES FOR AN EFFICIENT BUSINESS DATA ANALYSIS AND DECISION SUPPORT Janusz Kacprzyk, Ronald R. Yager and 1. Introduction 2. Idea of Linguistic Summaries Using Fuzzy Logic with Linguistic Quantifiers 3. On Other Validity Criteria 4. Derivation of Linguistic Summaries via a Fuzzy Logic Based Database Querying Interface 5. Implementation for a Sales Database at a Computer Retailer
89
111 113 118
125 126
126
129 129 131 134 140 147
CONTENTS
vii 6. Concluding Remarks References
Chapter 7
Chapter 8
