Knowledge discovery in inductive databases : third International Workshop, KDID 2004, Pisa, Italy, September 20, 2004 : revised selected and invited papers
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書誌事項
Knowledge discovery in inductive databases : third International Workshop, KDID 2004, Pisa, Italy, September 20, 2004 : revised selected and invited papers
（Lecture notes in computer science, 3377）
Springer, c2005
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Invited Paper.- Models and Indices for Integrating Unstructured Data with a Relational Database.- Contributed Papers.- Constraint Relaxations for Discovering Unknown Sequential Patterns.- Mining Formal Concepts with a Bounded Number of Exceptions from Transactional Data.- Theoretical Bounds on the Size of Condensed Representations.- Mining Interesting XML-Enabled Association Rules with Templates.- Database Transposition for Constrained (Closed) Pattern Mining.- An Efficient Algorithm for Mining String Databases Under Constraints.- An Automata Approach to Pattern Collections.- Implicit Enumeration of Patterns.- Condensed Representation of EPs and Patterns Quantified by Frequency-Based Measures.
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