Horizontal decompositions based on functional-dependency-set-implications
Proceedings on International conference on database theory
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Discovering roll-up dependencies
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
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In data mining, searching for frequent patterns is a common basic operation. It forms the basis of many interesting decision support processes. In this paper we present a new type of patterns, binary expressions. Based on the properties of a specified binary test, such as reflexivity, transitivity and symmetry, we construct a generic algorithm that mines all frequent binary expressions. We present three applications of this new type of expressions: mining for rules, for horizontal decompositions, and in intensional database relations. Since the number of binary expressions can become exponentially large, we use data mining techniques to avoid exponential execution times. We present results of the algorithm that show an exponential gain in time due to a well chosen pruning technique.