Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing the Interestingness of Association Rules from the Temporal Dimension
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
Mining changing customer segments in dynamic markets
Expert Systems with Applications: An International Journal
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
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Driven by the need to understand change within domains there is emerging research on methods which aim at analyzing how patterns and in particular itemsets evolve over time. In practice, however, these methods suffer from the problem that many of the observed changes in itemsets are temporally redundant in the sense that they are the side-effect of changes in other itemsets, hence making the identification of the fundamental changes difficult. As a solution we propose temporally closed itemsets, a novel approach for a condensed representation of itemsets which is based on removing temporal redundancies. We investigate how our approach relates to the well-known concept of closed itemsets if the latter would be directly generalized to account for the temporal dimension. Our experiments support the theoretical results by showing that the set of temporally closed itemsets is significantly smaller than the set of closed itemsets.