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,...
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Boston, MA :
Springer US,
2002.
|
| Σειρά: | The International Series in Engineering and Computer Science,
600 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
