Action Rules Discovery without Pre-existing Classification Rules
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Discovering Action Rules That Are Highly Achievable from Massive Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining Non-redundant Reclassification Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Constraint Based Action Rule Discovery with Single Classification Rules
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Action Rules Discovery Based on Tree Classifiers and Meta-actions
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Action Rules and the GUHA Method: Preliminary Considerations and Results
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Action rules discovery, a new simplified strategy
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Action rules discovery system DEAR_3
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
A distance-based approach for action recommendation
ECML'05 Proceedings of the 16th European conference on Machine Learning
Deterministic finite automata in the detection of EEG spikes and seizures
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Cooperative discovery of interesting action rules
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Post mining of diversified multiple decision trees for actionable knowledge discovery
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
An expected utility-based approach for mining action rules
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
Identifying the medical practice after total hip arthroplasty using an integrated hybrid approach
Computers in Biology and Medicine
Tree-based Construction of Low-cost Action Rules
Fundamenta Informaticae
Global Action Rules in Distributed Knowledge Systems
Fundamenta Informaticae - Concurrency Specification and Programming Workshop (CS&P'2001)
Mining actionable behavioral rules
Decision Support Systems
Pair-Based object-driven action rules
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Bayesian analysis of GUHA hypotheses
Journal of Intelligent Information Systems
Mining Meta-Actions for Action Rules Reduction
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
Causality-based cost-effective action mining
Intelligent Data Analysis
| Hi-index | 0.01 |
Decision tables classifying customers into groups of different profitability are used for mining rules classifying customers. Attributes are divided into two groups: stable and flexible. By stable attributes we mean attributes which values can not be changed by a bank (age, marital status, number of children are the examples). On the other hand attributes (like percentage rate or loan approval to buy a house in certain area) which values can be changed or influenced by a bank are called flexible. Rules are extracted from a decision table given preference to flexible attributes. This new class of rules forms a special repository of rules from which new rules called actionrules are constructed. They show what actions should be taken to improve the profitability of customers.