Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Information Sciences—Informatics and Computer Science: An International Journal
Efficient Rule-Based Attribute-Oriented Induction for Data Mining
Journal of Intelligent Information Systems
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
SAINTETIQ: a fuzzy set-based approach to database summarization
Fuzzy Sets and Systems - Data bases and approximate reasoning
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
An Attribute-Oriented Approach for Learning Classification Rules from Relational Databases
Proceedings of the Sixth International Conference on Data Engineering
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Indirect Association: Mining Higher Order Dependencies in Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Negative Association Rules
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
On the Mining of Substitution Rules for Statistically Dependent Items
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Clustering intrusion detection alarms to support root cause analysis
ACM Transactions on Information and System Security (TISSEC)
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
On multi-period multi-attribute decision making
Knowledge-Based Systems
MRM: A matrix representation and mapping approach for knowledge acquisition
Knowledge-Based Systems
Relations of attribute reduction between object and property oriented concept lattices
Knowledge-Based Systems
Business intelligence approach to supporting strategy-making of ISP service management
Expert Systems with Applications: An International Journal
Conceptual modeling rules extracting for data streams
Knowledge-Based Systems
Intrusion detection alarms reduction using root cause analysis and clustering
Computer Communications
A Multicriteria Approach to Data Summarization Using Concept Ontologies
IEEE Transactions on Fuzzy Systems
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
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Attribute-oriented induction (AOI) is a useful data mining method for extracting generalized knowledge from relational data and users' background knowledge. Concept hierarchies can be integrated with the AOI method to induce multi-level generalized knowledge. However, the existing AOI approaches are only capable of mining positive knowledge from databases; thus, rare but important negative generalized knowledge that is unknown, unexpected, or contradictory to what the user believes, can be missed. In this study, we propose a global negative attribute-oriented induction (GNAOI) approach that can generate comprehensive and multiple-level negative generalized knowledge at the same time. Two pruning properties, the downward level closure property and the upward superset closure property, are employed to improve the efficiency of the algorithm, and a new interest measure, nim(cl), is exploited to measure the degree of the negative relation. Experiment results from a real-life dataset show that the proposed method is effective in finding global negative generalized knowledge.