C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Inductive databases and condensed representations for data mining (extended abstract)
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Automatic Indexing: An Experimental Inquiry
Journal of the ACM (JACM)
Machine Learning
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Itemset Trees for Targeted Association Querying
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Knowledge Discovery in Inductive Databases: 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers (Lecture Notes in Computer Science)
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Private itemset support counting
ICICS'05 Proceedings of the 7th international conference on Information and Communications Security
Data mining in inductive databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Data & Knowledge Engineering
Frequent pattern mining and knowledge indexing based on zero-suppressed BDDs
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Finding extremal sets on the GPU
Journal of Parallel and Distributed Computing
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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The purpose of this paper is two-fold: First, we give efficient algorithms for answering itemset support queries for collections of itemsets from various representations of the frequency information. As index structures we use itemset tries of transaction databases, frequent itemsets and their condensed representations. Second, we evaluate the usefulness of condensed representations of frequent itemsets to answer itemset support queries using the proposed query algorithms and index structures. We study analytically the worst-case time complexities of querying condensed representations and evaluate experimentally the query efficiency with random itemset queries to several benchmark transaction databases.