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 34
... monotonic and anti-monotonic constraints and their boolean combinations. The GUHA Method, Data Preprocessing and Mining surveys the basic principles and foundations of the GUHA method, the available systems and related works. This ...
... monotonic and anti-monotonic selection predicates. This work proves important for inductive and database systems for data mining since it deals with sets of queries, whereas previous work in maximal, closed and condensed representations ...
... monotonic constraints [64,51,18]. A second major issue is the possibility to approximate the results of (extended) inductive queries. This approximation can concern a collection of patterns that is a superset or a subset of the desired ...
... monotonic constraints [55,64], e.g., the minimal frequency constraint. It relies on the fact that if an itemset violates an anti-monotonic constraint then all its supersets violate it as well and therefore this itemset and its supersets ...
... monotonic constraint is a constraint C such that for all itemsets S, S: (S⊆ S ∧ S satisfies C) ⇒ S satisfies C. The negation of an anti-monotonic constraint is a monotonic constraint and the conjunction or disjunction of monotonic ...
Contents
1 | |
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 |