Knowledge Discovery in Inductive Databases: Third International Workshop, KDID 2004, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers
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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Models and Indices for Integrating Unstructured Data with a Relational Database
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
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anti-monotonic automata automaton bi-set boolean closed itemsets closed patterns closed sets closure concept mining concepts conditional random fields Conf context-free languages Data Mining database scan dataset defined Definition denoted discovered patterns Discovery and Data disjunction-free sets efficient emerging patterns example extraction FAVST fr(X free itemsets frequency-based measure frequent closed frequent itemsets frequent patterns Galois connection Goethals growth rate implicit inductive databases interestingness values International Conference Intl itemset collection itemset mining itemset representation itemsets satisfying Knowledge Discovery l-free sets label Mannila maximal minimum frequency threshold Mining association rules monotonic node non-derivable itemsets pattern collections pattern mining predicates problem Proc properties pruning pushdown pushdown automaton queries representations of frequent represented Sarawagi Section SEPs sequence sequential pattern mining SIGKDD SIGMOD string strong emerging structure subsets substring patterns supp(abc supp(I,D transposed constraint trie version space XML-enabled association rule