Knowledge discovery for business information systems

Knowledge discovery for business information systems / edited by Witold Abramowicz, Jozef Zurada.

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,...

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Bibliographic Details
Other Authors: Abramowicz, Witold, Zurada, Jozef, 1949-
Format: eBook
Language:English
Published: New York : Kluwer Academic Publishers, ©2001.
Series:Kluwer international series in engineering and computer science ; SECS 600.
Subjects:
Data mining.
Knowledge acquisition (Expert systems)
Database searching.
online searching.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Data mining
Database searching
Online Access:Click for online access
Table of Contents:
  • Cover
  • Table of Contents
  • Preface
  • Foreword
  • List of Contributors
  • Chapter 1 Information Filters Suppliying Data Warehouses with Benchmarking Information
  • 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
  • Chapter 2 Parallel Mining of Association Rules
  • 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
  • Chapter 3 Unsupervised Feature Ranking and Selection
  • 1. Introduction
  • 2. Basic Concepts and Possible Approaches
  • 3. An Entropy Measure for Continuous and Nominal Data Types
  • 4. Algorithm to Find Important Variables
  • 5. Experimental Studies
  • 6. Clustering Using SUD
  • 7. Discussion and Conclusion
  • References
  • Chapter 4 Approaches to Concept Based Exploration of Information Resources
  • 1. Introduction
  • 2. Conceptual Taxonomies
  • 3. Ontology Driven Concept Retrieval
  • 4. Search based on formal concept analysis
  • 5. Conclusion
  • Acknowledgements
  • References
  • Chapter 5 Hybrid Methodology of Knowledge Discovery for Business Information
  • 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
  • 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
  • 6. Concluding Remarks
  • References
  • Chaper 7 Integrating Data Sources Using a Standardized Global Directory
  • 1. Introduction
  • 2. Data Semantics and the Integration Problem
  • 3. Previous work
  • 4. The Integration Architecture
  • 5. The Global Dictionary
  • 6. The Relational Integration Model
  • 7. Special Cases of Integration
  • 8. Applications to the WWW
  • 9. Future Work and Conclusions
  • References
  • Chapter 8 Maintenance of Discovered Association Rules
  • 1. Introduction
  • 2. Problem Description
  • 3. The FUP Algorithm for the Insertion Only Case
  • 4. The FUP Algorithm for the Deletions Only Case
  • 5. The FUP2 Algorithm for the General Case
  • 6. Performance Studies
  • 7. Discussions
  • 8. Conclusions
  • Notes
  • References
  • Chapter 9 Multidimensional Business Process Analysis with the Process Warehouse
  • 1. Introduction
  • 2. Related Work
  • 3. Goals of the Data Warehouse Approach
  • 4. Data Source
  • 5. Basic Process Warehouse Components Representing Business Process Analysis Requirements
  • 6. Data Model a.

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