Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Statistics and data mining techniques for lifetime value modeling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple Comparisons in Induction Algorithms
Machine Learning
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Machine Learning
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Using a Permutation Test for Attribute Selection in Decision Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Significance Tests for Patterns in Continuous Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Determining Hit Rate in Pattern Search
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
An iterative hypothesis-testing strategy for pattern discovery
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Significant Patterns
Machine Learning
Statistical mining of interesting association rules
Statistics and Computing
Privacy-preserving statistical quantitative rules mining
Proceedings of the 2nd international conference on Scalable information systems
Correlated pattern mining in quantitative databases
ACM Transactions on Database Systems (TODS)
An information-theoretic approach to quantitative association rule mining
Knowledge and Information Systems
Measuring interestingness of discovered skewed patterns in data cubes
Decision Support Systems
An intelligent questionnaire analysis expert system
Expert Systems with Applications: An International Journal
Multi-level Frequent Pattern Mining
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Issues in pattern mining and their resolutions
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
An efficient rigorous approach for identifying statistically significant frequent itemsets
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
ACM Transactions on Knowledge Discovery from Data (TKDD)
Interestingness of Association Rules Using Symmetrical Tau and Logistic Regression
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Minimum variance associations: discovering relationships in numerical data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
Multiple hypothesis testing in pattern discovery
DS'11 Proceedings of the 14th international conference on Discovery science
From information to operations: Service quality and customer retention
ACM Transactions on Management Information Systems (TMIS)
Distribution rules with numeric attributes of interest
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets
Journal of the ACM (JACM)
International Journal of Information Management: The Journal for Information Professionals
Significant motifs in time series
Statistical Analysis and Data Mining
Mining sequential patterns with extensible knowledge representation
Intelligent Data Analysis
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In this paper we study market share rules, rules that have a certain market share statistic associated with them. Such rules are particularly relevant for decision making from a business perspective. Motivated by market share rules, in this paper we consider statistical quantitative rules (SQ rules) that are quantitative rules in which the RHS can be any statistic that is computed for the segment satisfying the LHS of the rule. Building on prior work, we present a statistical approach for learning all significant SQ rules, i.e., SQ rules for which a desired statistic lies outside a confidence interval computed for this rule. In particular we show how resampling techniques can be effectively used to learn significant rules. Since our method considers the significance of a large number of rules in parallel, it is susceptible to learning a certain number of "false" rules. To address this, we present a technique that can determine the number of significant SQ rules that can be expected by chance alone, and suggest that this number can be used to determine a "false discovery rate" for the learning procedure. We apply our methods to online consumer purchase data and report the results.