Data Mining deals with discovering hidden knowlege, unexpected patterns and rules in large databases. It can bring significant gains to organizations, for example, through better targeted marketing and enhanced internal performance. If you have large data sets (for example, large quantities of financial data, extensive customer databases or sales records) you can benefit from this newly emerging field. But setting up a data mining environment is not a trivial task. The long-term goal must be to create a self-learning organization that makes optimal use of the information it generates.
This is the first book to offer a comprehensive introduction to data mining. Its aim is to provide essential insights and guidelines to help you make the right decisions when setting up a data mining environment.
It offers answers to questions such as:
- What is Data Mining?
- Which techniques are suitable for my data?
- How do I set up a data mining environment?
- How do I justify the costs?
The whole data mining process, including data selection, cleaning, coding, different pattern recognition techniques and reporting, is illustrated by means of an extensive case study and numerous answers.
- General management and IT managers
Table of Contents:
(Most chapters begin with "Introduction", while all chapters finish with "Conclusion".)
Overview of the Book.
An Expanding Universe of Data. 2. What is Learning?
Information as a Production Factor.
Computer Systems That Can Learn.
Data Mining Versus Query Tools.
Data Mining in Marketing.
Practical Applications of Data Mining.
Introduction. 3. Data Mining and the Data Warehouse.
What is Learning?
Self-Learning Computer Systems.
Machine Learning and the Methodology of Science.
A Kangaroo in Mist.
Introduction. 4. The Knowledge Discovery Process.
What is a Data Warehouse and Why Do We Need It?
Designing Decision Support Systems.
Integration With Data Mining.
Client/Server and Data Warehousing.
Introduction. 5. Setting Up a Kdd Environment.
The Knowledge Discovery Process in Detail.
Preliminary Analysis of the Data Set Using Traditional query tools.
Likelihood and Distance.
Introduction. 6. Some Real-Life Applications.
Different Forms of Knowledge.
The KDD Environment.
Ten Golden Rules.
Introduction. 7. Some Formal Aspects of Learning Algorithms.
Predicting Bid Behaviour of Pilots.
Discovering Foreign Key Relationships.
Introduction. Summary. Glossary. Further Reading. Index.
Learning as Compression of Data Sets.
The Information Content of a Message.
Noise and Redundancy.
The Significance of Noise.
The Traditional Theory of the Relational Database.
From Relations To Tables.
From Keys To Statistical Dependencies.
Data Mining Primitives.