Relational Database Data Mining
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Relational Data Mining
Author: Saso Dzeroski
language: en
Publisher: Springer Science & Business Media
Release Date: 2001-08
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Database Support for Data Mining Applications
Author: Rosa Meo
language: en
Publisher: Springer Science & Business Media
Release Date: 2004-07-28
Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.
Granular-Relational Data Mining
This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining.