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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... Pier Luca Lanzi Mika Klemettinen (Eds.) Database Support for Data Mining Applications Discovering Knowledge With Inductive Queries e[B]e(D) © Springer Lecture Notes in Artificial Intelligence 2682 Edited by J. G.. Rosa Meo Front Cover.
... Artificial Intelligence 2682 Edited by J. G. Carbonell and J. Siekmann Subseries of Lecture Notes in Computer Science Rosa Meo Pier Luca Lanzi Mika Klemettinen (Eds.) Database Support.
... machine learning concept of linguistic bias (see, e.g., [63]). Let us consider primitive constraints based on frequency. First, we can enforce that a given pattern is frequent enough (Cminfreq(S)) and then we specify that a given ...
... machine learning terminology [60]. Also, it is possible to compute these maximal itemsets and their frequencies without computing every frequency of every frequent itemsets (see, e.g., [6]). This can be generalized to any antimonotonic ...
... Machine Learning, 17(1):5–46, 1994. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of ... Artificial Intelligence, 52(2):151–168, 1992. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 22 J.-F. Boulicaut.
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 |