Data Mining in Large Databases Using Domain Generalization Graphs
Journal of Intelligent Information Systems
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Adding Temporal Semantics to Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A Technique for Generalizing Temporal Durations in Relational Databases
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Managing time granularity of narrative clinical information: the temporal data model TIME-NESIS
TIME '96 Proceedings of the 3rd Workshop on Temporal Representation and Reasoning (TIME'96)
Querying Multiple Temporal Granularity Data
TIME '00 Proceedings of the Seventh International Workshop on Temporal Representation and Reasoning (TIME'00)
Attribute-Oriented Induction Using Domain Generalization Graphs
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
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This paper addresses the problem of data mining from temporal data based on calendar (date and time) attributes. The proposed methods uses a probabilistic domain generalization graph, i.e., a graph defining a partial order that represents a set of generalization relations for an attribute, with an associated probability distribution for the values in the domain represented by each of its nodes. We specify the components of a domain generalization graph suited to calendar attributes and define granularity, subset, lookup, and algorithmic methods for specifying generalizations between calendar domains. We provide a means of specifying distributions. We show how the calendar DGG can be applied to a data mining problem to produce a list of summaries ranked according to an interest measure given assumed probability distributions.