Database Support for Data Mining Applications: Discovering Knowledge with Inductive QueriesRosa Meo, Pier L. Lanzi, Mika Klemettinen Springer Science & Business Media, 2004 M07 28 - 323 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. |
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... Query Execution Inductive Databases and Multiple Uses of Frequent Itemsets: the cInQ Approach ... Language for Data Mining .................... 76 Fosca Giannotti, Giuseppe Manco, Franco Turini A Data Mining Query Language for Knowledge ...
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Contents
Database Languages and Query Execution | 1 |
A Comparative | 20 |
Declarative Data Mining Using SQL3 | 52 |
Towards a Logic Query Language for Data Mining | 76 |
A Data Mining Query Language for Knowledge Discovery in | 95 |
Towards Query Evaluation in Inductive Databases Using Version Spaces | 117 |
The GUHA Method Data Preprocessing and Mining | 135 |
Constraint Based Mining of First Order Sequences in SeqLog | 154 |
Frequent Itemset Discovery with SQL Using Universal Quantification | 194 |
Deducing Bounds on the Support of Itemsets | 214 |
ModelIndependent Bounding of the Supports of Boolean Formulae | 234 |
Condensed Representations for Sets of Mining Queries | 250 |
OneSided InstanceBased Boundary Sets | 270 |
Domain Structures in Filtering Irrelevant Frequent Patterns | 289 |
Kimmo Hatonen Mika Klemettinen | 304 |
324 | |
Other editions - View all
Database Support for Data Mining Applications: Discovering Knowledge with ... Rosa Meo,Pier L. Lanzi,Mika Klemettinen No preview available - 2014 |