Knowledge Discovery in Inductive Databases: Third International Workshop, KDID 2004, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers

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Springer Science & Business Media, Feb 23, 2005 - Computers - 189 pages
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 onPrinciplesandPracticeofKnowledgeDiscoveryinDatabases(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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Contents

Models and Indices for Integrating Unstructured Data with a Relational Database
1
Constraint Relaxations for Discovering Unknown Sequential Patterns
11
Mining Formal Concepts with a Bounded Number of Exceptions from Transactional Data
33
Theoretical Bounds on the Size of Condensed Representations
46
Templates
66
Pattern Mining
89
An Efficient Algorithm for Mining String Databases Under Constraints
108
An Automata Approach to Pattern Collections
130
Implicit Enumeration of Patterns
150
Condensed Representation of EPs and Patterns Quantified by FrequencyBased Measures
173
Author Index
190
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