Data mining and the data warehouse The knowledge discovery process Setting up a KDD environment Some real-life applications Some formal aspects of learning algorithms, Most chapters begin with "Introduction", while all chapters finish with "Conclusion". Overview of the Book. An Expanding Universe of Data. Information as a Production Factor. Computer Systems That Can Learn. Data Mining.

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Data mining and the data warehouse The knowledge discovery process Setting up a KDD environment Some real-life applications Some formal aspects of learning algorithms, Most chapters begin with "Introduction", while all chapters finish with "Conclusion".
Overview of the Book. An Expanding Universe of Data. Information as a Production Factor. Computer Systems That Can Learn. Data Mining. Data Mining Versus Query Tools. Data Mining in Marketing. Practical Applications of Data Mining. What is Learning? Self-Learning Computer Systems. Machine Learning and the Methodology of Science. Concept Learning. A Kangaroo in Mist. Data Mining and the Data Warehouse.
Designing Decision Support Systems. Integration With Data Mining. Multi-Processing Machine. Cost Justification. The Knowledge Discovery Process. The Knowledge Discovery Process in Detail. Data Selection. Visualization Techniques.
Likelihood and Distance. OLAP Tools. K-Nearest Neighbour. Decision Trees. Association Rules. Neural Networks. Genetic Algorithms. Setting Up a Kdd Environment. Different Forms of Knowledge. Getting Started. The KDD Environment. Ten Golden Rules. Some Real-Life Applications. Customer Profiling.
Predicting Bid Behaviour of Pilots. Discovering Foreign Key Relationships. Some Formal Aspects of Learning Algorithms. Learning as Compression of Data Sets. The Information Content of a Message. Noise and Redundancy. The Significance of Noise.
Fuzzy Databases. The Traditional Theory of the Relational Database. From Relations To Tables. From Keys To Statistical Dependencies.
Data Mining Primitives. Further Reading.
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Data mining and the data warehouse. Other Authors Zantinge, Dolf. Setting up a data mining environment is not a trivial task, and the long term aim is to create a self-learning organization that makes optimal use of the information it generates. Ir directamente al contenido principal.
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