A bayesian scoring technique for mining predictive and non-spurious rules

Authors:
Iyad Batal;Gregory Cooper;Milos Hauskrecht
Affiliations:
Department of Computer Science, University of Pittsburgh;Department of Biomedical Informatics, University of Pittsburgh;Department of Computer Science, University of Pittsburgh
Venue:
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Year:
2012

Citing 22
Cited 0

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Beyond market baskets: generalizing association rules to correlations

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A simple, fast, and effective rule learner

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine learning in automated text categorization

ACM Computing Surveys (CSUR)
Detecting Group Differences: Mining Contrast Sets

Data Mining and Knowledge Discovery
An Information Theoretic Approach to Rule Induction from Databases

IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining

IEEE Transactions on Knowledge and Data Engineering
The CN2 Induction Algorithm

Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

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
Mining Optimal Class Association Rule Set

PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Constraint-Based Rule Mining in Large, Dense Databases

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Frequent Substructure-Based Approaches for Classifying Chemical Compounds

IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey

ACM Computing Surveys (CSUR)
Extracting redundancy-aware top-k patterns

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Significant Patterns

Machine Learning
A two-stage methodology for sequence classification based on sequential pattern mining and optimization

Data & Knowledge Engineering
Correlated itemset mining in ROC space: a constraint programming approach

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

The Journal of Machine Learning Research
Subgroup discovery for election analysis: a case study in descriptive data mining

DS'10 Proceedings of the 13th international conference on Discovery science
Relevancy in constraint-based subgroup discovery

Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods.