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 25
... DMQL, MSQL, MINE RULE, and standardization efforts for coupling database technology and data mining systems, such as OLEDB-DM and PMML. Declarative Data Mining Using SQL-3 shows a new approach, compared to existing SQL approaches, to ...
... DMQL [38], and MINE RULE [59]) in the light of the IDBs' requirements. In this paper, we focus on mining queries, the so-called inductive queries, i.e., queries that return patterns from a given database. More precisely, we consider the ...
... DMQL [38], and MINE RULE [59]). Among them, the MINE RULE query language is one of the few proposals for which a formal operational semantics has been published [59]. Ideally, these query languages must support not only the selection of ...
... DMQL and MINE RULE) that have been proposed for descriptive rule mining and discuss their common features and differences. These query languages look like extensions of SQL. We present them using a set of examples, taken from the real ...
... DMQL [10,11] and MINE RULE [12,13]. In the paper we discuss also OLE DB for Data Mining (OLE DB DM) by Microsoft and Predictive Model Markup Language (PMML) by Data Mining Group [18]. OLE DB DM is an Application Programming Interface ...
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 |