Database Support for Data Mining Applications: Discovering Knowledge with Inductive Queries (Lecture Notes in Computer Science)

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. Front Matter....Pages - Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach....Pages 1-23 Query Languages Supporting Descriptive Rule Mining: A Comparative Study....Pages 24-51 Declarative Data Mining Using SQL3....Pages 52-75 Towards a Logic Query Language for Data Mining....Pages 76-94 A Data Mining Query Language for Knowledge Discovery in a Geographical Information System....Pages 95-116 Towards Query Evaluation in Inductive Databases Using Version Spaces....Pages 117-134 The GUHA Method, Data Preprocessing and Mining....Pages 135-153 Constraint Based Mining of First Order Sequences in SeqLog....Pages 154-173 Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS....Pages 174-193 Frequent Itemset Discovery with SQL Using Universal Quantification....Pages 194-213 Deducing Bounds on the Support of Itemsets....Pages 214-233 Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data....Pages 234-249 Condensed Representations for Sets of Mining Queries....Pages 250-269 One-Sided Instance-Based Boundary Sets....Pages 270-288 Domain Structures in Filtering Irrelevant Frequent Patterns....Pages 289-305 Integrity Constraints over Association Rules....Pages 306-323 Back Matter....Pages -

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.

Knowledge Discovery in Databases (KDD) is a complex interactive process which involves many steps that must be done sequentially.