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
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... examples of transactional databases concern basket data (transactions are sets of products that are bought by ... example of a transactional database and some information about itemsets within this database. Association rules are ...
... Example 1. In the database of Figure 1, let us 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 ...
... Example 3. Considering the database of Figure 1, if Cminfreq specifies that an itemset (or a rule) must be 0.6-frequent, then SATCminfreq = {A,B,C,AC,BC}. For rules, if the confidence threshold is 0.7, then the frequent and valid rules ...
... Example 5. In the database of Figure 1, the closed itemsets are C, AC, BC, ABC, and ABCD. Free itemsets are sets of items that are not “strongly” correlated [15]. They have been designed as a useful intermediate representation for ...
... Example 8. Provided the dataset of Figure 1 and the constraints from Example 3 and 4, SATCminfreq∧Csize∧Cmiss = {A,C,AC} is returned when the query specifies that the desired itemsets must be 0.6-frequent, with size less than 3 and ...
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 |