Knowledge Discovery Inductive Databases (48 results)
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Condition: New. Presents the thoroughly refereed joint postproceedings of the 4th International Workshop on Knowledge Discovery in Inductive DatabasesThe papers address a range of current topics in knowledge discovery and data mining in the framework of inductive.
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Condition: New. This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine learnin.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The3rdInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (KDID 2004) was held in Pisa, Italy, on September 20, 2004 as part of the 15th European Conference on Machine Learning and the 8th European Conference onPrinciplesandPracticeofKnow…ledgeDiscoveryinDatabases(ECML/PKDD 2004). Ever since the start of the eld of data mining, it has been realized that the knowledge discovery and data mining process should be integrated into database technology. This idea has been formalized in the concept of inductive databases, introduced by Imielinski and Mannila (CACM 1996, 39(11)). In general, an inductive database is a database that supports data mining and the knowledge discovery process in a natural and elegant way. In addition to the usual data, it also contains inductive generalizations (e.g., patterns, models) extracted from the data. Within this framework, knowledge discovery is an - teractive process in which users can query the inductive database to gain insight to the data and the patterns and models within that data. Despite many recent developments, there still exists a pressing need to - derstandthecentralissuesininductivedatabases.Thisworkshopaimedtobring together database and data mining researchers and practitioners who are int- ested in the numerous challenges that inductive databases o ers. This workshop followed the previous two workshops: KDID 2002 held in Helsinki, Finland, and KDID 2003 held in Cavtat-Dubrovnik, Croatia.
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Taschenbuch. Condition: Neu. Knowledge Discovery in Inductive Databases | 5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers | Saso Dzeroski (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2007 | Springer | EAN 9783540755487 | Verantwortliche Person für… die EU: Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg, productsafety[at]springernature[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine l…earning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
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Taschenbuch. Condition: Neu. Neuware - The4thInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (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.
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