ExAnte: A Preprocessing Method for Frequent-Pattern Mining

Our main research objective is to define a data mining query language, supported by a system that can optimize constraint-based data mining queries. We have invented ExAnte, a simple yet effective preprocessing technique for frequent-pattern mining. ExAnte exploits constraints to dramatically reduce the analyzed data to those containing patterns of interest. This data reduction, in turn, induces a strong reduction of the candidate patterns' search space, thus supporting substantial performance improvements in subsequent mining.

Authors

• F. Bonchi
• F. Giannotti
• A. Mazzanti
• D. Pedreschi

Keywords

• Data mining
• Data analysis
• Itemsets
• Pattern analysis
• Frequency
• Association rules
• Laboratories
• Floods
• Constraint optimization
• Information filtering
• Frequent Pattern Mining
• Search Space
• Data Reduction
• Kalman Filter
• Interesting Patterns
• Knowledge Discovery
• Search Problem
• Mining System
• Total Sum
• Frequent Pattern
• Work Unit
• Frequency Threshold
• Minimum Constraint
• Association Rule Mining
• Total Price
• Frequent Itemsets
• Apriori Algorithm
• Input Database
• frequent-pattern mining
• constraints
• preprocessing