Knowledge Discovery for Business Information Systems

Knowledge Discovery for Business Information Systems edited by Witold Abramowicz, Jozef M 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
Corporate Author: SpringerLink (Online service)
Other Authors: Abramowicz, Witold (Editor), Zurada, Jozef M. (Editor)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 2002.
Edition:1st ed. 2002.
Series:The Springer International Series in Engineering and Computer Science, 600
Springer eBook Collection.
Subjects:
Data structures (Computer science).
Information technology.
Business—Data processing.
Artificial intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Information Filters Supplying Data Warehouses with Benchmarking Information
  • Parallel Mining of Association Rules
  • Unsupervised Feature Ranking and Selection
  • Approaches to Concept Based Exploration of Information Resources
  • Hybrid Methodology of Knowledge Discovery for Business Information
  • Fuzzy Linguistic Summaries of Databases for an Efficient Business Data Analysis and Decision Support
  • Integrating Data Sources Using a Standardized Global Dictionary
  • Maintenance of Discovered Association Rules
  • Multidimensional Business Process Analysis with the Process Warehouse
  • Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeling
  • Robust Business Intelligence Solutions
  • The Role of Granular Information in Knowledge Discovery in Databases
  • Dealing with Dimensions in Data Warehousing
  • Enhancing the KDD Process in the Relational Database Mining Framework by Quantitative Evaluation of Association Rules
  • Speeding up Hypothesis Development
  • Sequence Mining in Dynamic and Interactive Environments
  • Investigation of Artificial Neural Networks for Classifying Levels of Financial Distress of Firms: The Case of an Unbalanced Training Sample.

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