000 04195nam a22005055i 4500
001 978-3-540-33293-0
003 DE-He213
005 20160624101928.0
007 cr nn 008mamaa
008 100301s2006 gw | s |||| 0|eng d
020 _a9783540332930
_9978-3-540-33293-0
024 7 _a10.1007/11733492
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aUMT
_2bicssc
072 7 _aCOM021000
_2bisacsh
082 0 4 _a005.74
_223
245 1 0 _aKnowledge Discovery in Inductive Databases
_h[electronic resource] :
_b4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers /
_cedited by Francesco Bonchi, Jean-François Boulicaut.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2006.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2006.
300 _aVIII, 252 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v3933
505 0 _aInvited Papers -- Data Mining in Inductive Databases -- Mining Databases and Data Streams with Query Languages and Rules -- Contributed Papers -- Memory-Aware Frequent k-Itemset Mining -- Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data -- Experiment Databases: A Novel Methodology for Experimental Research -- Quick Inclusion-Exclusion -- Towards Mining Frequent Queries in Star Schemes -- Inductive Databases in the Relational Model: The Data as the Bridge -- Transaction Databases, Frequent Itemsets, and Their Condensed Representations -- Multi-class Correlated Pattern Mining -- Shaping SQL-Based Frequent Pattern Mining Algorithms -- Exploiting Virtual Patterns for Automatically Pruning the Search Space -- Constraint Based Induction of Multi-objective Regression Trees -- Learning Predictive Clustering Rules.
520 _aThe4thInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (KDID 2005) was held in Porto, Portugal, on October 3, 2005 in conjunction with the 16th European Conference on Machine Learning and the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Ever since the start of the ?eld of data mining, it has been realized that the integration of the database technology into knowledge discovery processes was a crucial issue. This vision has been formalized into the inductive database perspective introduced by T. Imielinski and H. Mannila (CACM 1996, 39(11)). The main idea is to consider knowledge discovery as an extended querying p- cess for which relevant query languages are to be speci?ed. Therefore, inductive databases might contain not only the usual data but also inductive gener- izations (e. g. , patterns, models) holding within the data. Despite many recent developments, there is still a pressing need to understand the central issues in inductive databases. Constraint-based mining has been identi?ed as a core technology for inductive querying, and promising results have been obtained for rather simple types of patterns (e. g. , itemsets, sequential patterns). However, constraint-based mining of models remains a quite open issue. Also, coupling schemes between the available database technology and inductive querying p- posals are not yet well understood. Finally, the de?nition of a general purpose inductive query language is still an on-going quest.
650 0 _aComputer science.
650 0 _aDatabase management.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aDatabase Management.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aBonchi, Francesco.
_eeditor.
700 1 _aBoulicaut, Jean-François.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540332923
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v3933
856 4 0 _uhttp://dx.doi.org/10.1007/11733492
942 _2EBK4041
_cEBK
999 _c33335
_d33335