Database Support for Data Mining Applications: Discovering Knowledge with Inductive QueriesRosa Meo, Pier L. Lanzi, Mika Klemettinen Springer, 2004 M07 28 - 332 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. |
From inside the book
Results 1-5 of 82
... computation of the specified theory while post-processing can be considered as a querying activity on a materialized ... compute the (extended) theories and that have good properties in practice (e.g., scalability w.r.t. the size of the ...
... computed from a sample of the data (see, e.g., [74]) or when a relaxed constraint is used. Another important case of approximation for extended theories is the exact computation of the underlying theory while the evaluation functions ...
... compute closure(AB). Items A and B occur in transactions 1, 5 and 6. Item C is the only other item that is also present in these transactions, thus closure(AB) = ABC. Also, closure(A) = AC, closure(B) = BC, and closure(BC) = BC. We now ...
... computation of SATCminfreq for a given frequency threshold. The standard association rule mining problem introduced in [1] is to find all the association rules that verify the minimal frequency and minimal confidence constraints for ...
... computation of every itemset such that Cminfreq(S,r 1) ∧ Cmaxfreq(S,r 2). Indeed, these itemsets are supported by ... compute various condensed representations of the frequent itemsets: Close [65], Closet[69], Charm [75], Inductive ...
Contents
1 | |
A Comparative | 24 |
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 |
Arnaud Giacometti Dominique Laurent Cheikh Talibouya Diop | 270 |
Evgueni N Smirnov Ida G SprinkhuizenKuyper H Japp van den Herik | 289 |
Kimmo Hätönen Mika Klemettinen | 304 |
Artur Bykowski Thomas Daurel Nicolas Méger Christophe Rigotti | 324 |