Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Measuring lift quality in database marketing
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
What's interesting about Cricket?: on thresholds and anticipation in discovered rules
ACM SIGKDD Explorations Newsletter
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
ACM SIGKDD Explorations Newsletter
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
From Path Tree To Frequent Patterns: A Framework for Mining Frequent Patterns
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Multi-scaling sampling: an adaptive sampling method for discovering approximate association rules
Journal of Computer Science and Technology
Computing the minimum-support for mining frequent patterns
Knowledge and Information Systems
A new sampling technique for association rule mining
Journal of Information Science
Association rule mining: models and algorithms
Association rule mining: models and algorithms
Is frequency enough for decision makers to make decisions?
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Progressive sampling for association rules based on sampling error estimation
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Effective sampling for mining association rules
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Constrained frequent pattern mining on univariate uncertain data
Journal of Systems and Software
| Hi-index | 0.00 |
There are many advanced techniques that can efficiently mine frequent itemsets using a minimum-support. However, the question that remains unanswered is whether the minimum-support can really help decision makers to make decisions. In this paper, we study four summary queries for frequent itemsets mining, namely, (1) finding a support-average of itemsets, (2) finding a support-quantile of itemsets, (3) finding the number of itemsets that greater/less than the support-average, i.e., an approximated distribution of itemsets, and (4) finding the relative frequency of an itemset (compared its frequency with that of other itemsets in the same dataset). With these queries, a decision maker will know whether an itemset in question is greater/less than the support-quantile; the distribution of itemsets; and the frequentness of an itemset. Processing these summary queries is challenging, because the minimum-support constraint cannot be used to prune infrequent itemsets. In this paper, we propose several simple yet effective approximation solutions. We conduct extensive experiments for evaluating our strategy, and illustrate that the proposed approaches can well model and capture the statistical parameters (summary queries) of itemsets in a database.