Discovery in multi-attribute data with user-defined constraints
ACM SIGKDD Explorations Newsletter
Demand-driven frequent itemset mining using pattern structures
Knowledge and Information Systems
Discovering actionable patterns in event data
IBM Systems Journal
MCFPTree: An FP-tree-based algorithm for multi-constraint patterns discovery
International Journal of Business Intelligence and Data Mining
Expert Systems with Applications: An International Journal
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Mining for frequent itemsets typically involves a preprocessing step in which data with multiple attributes are grouped into transactions, and item are defined based on attribute values. We have observed that such fixed attribute mining can severely constrain the pattern that are discovered. Herein, we introduce mining paces, a new framework for mining multi-attribute data that include the discovery of transaction and item definition (with the exploitation of taxonomies and functional dependenciesif they are available).We prove that special downward closure properties (or anti-monotonic property) hold for mining paces, aresult that allows us to construct efficient algorithms for mining pattern without the constraint of fixed attribute mining. We apply our algorithm to real world data collected from a production computer network. The result how that by exploiting the special kind of downward closure in mining paces, execution times for mining can be reduced by a factor of three to four.