Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A logical language for data and knowledge bases
A logical language for data and knowledge bases
Categories, types, and structures: an introduction to category theory for the working computer scientist
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Advances in Distributed and Parallel Knowledge Discovery
Advances in Distributed and Parallel Knowledge Discovery
Microarrays for an Integrative Genomics
Microarrays for an Integrative Genomics
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
The Haar Wavelet Transform in the Time Series Similarity Paradigm
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
MAMBO: Discovering Association Rules Based on Conditional Independencies
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Data Mining in Time Series Database
Data Mining in Time Series Database
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Constructing (Almost) phylogenetic trees from developmental sequences data
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Learning Bayesian Networks
Aggregating learned probabilistic beliefs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
IQL: a proposal for an inductive query language
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Itemset support queries using frequent itemsets and their condensed representations
DS'06 Proceedings of the 9th international conference on Discovery Science
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Ever since the seminal paper by Imielinski and Mannila [11], inductive databases have been a constant theme in the data mining literature. Operationally, such an inductive database is a database in which models and patterns are first class citizens. In the extensive literature on inductive databases there is at least one consequence of this operational definition that is conspicuously missing. That is the question: if we have models and patterns in our inductive database, how does this help to discover other models and patterns? This question is the topic of this paper.