The Utility of Knowledge in Inductive Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Machine Learning
Flexibly exploiting prior knowledge in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining interesting knowledge using DM-II
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-level organization and summarization of the discovered rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploration mining in diabetic patients databases: findings and conclusions
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Web for data mining: organizing and interpreting the discovered rules using the Web
ACM SIGKDD Explorations Newsletter
Discovering unexpected information from your competitors' web sites
Proceedings of the seventh 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
ADC '01 Proceedings of the 12th Australasian database conference
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Toward Multidatabase Mining: Identifying Relevant Databases
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Visualization Support for Data Mining
IEEE Expert: Intelligent Systems and Their Applications
Web for Data Mining Applications
COMPSAC '00 24th International Computer Software and Applications Conference
Mining Changes for Real-Life Applications
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Domain knowledge to support the discovery process: previously discovered knowledge
Handbook of data mining and knowledge discovery
Domain knowledge to support the discovery process: user preferences
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Finding unexpected patterns in data
Data mining, rough sets and granular computing
Building an Association Rules Framework to Improve Product Assortment Decisions
Data Mining and Knowledge Discovery
Mining unexpected rules by pushing user dynamics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Experiences in building a tool for navigating association rule result sets
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
Using metarules to organize and group discovered association rules
Data Mining and Knowledge Discovery
AISIID: An artificial immune system for interesting information discovery on the web
Applied Soft Computing
A change detection model for credit card usage behavior
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Conceptual equivalence for contrast mining in classification learning
Data & Knowledge Engineering
Measuring interestingness of discovered skewed patterns in data cubes
Decision Support Systems
Mining the change of event trends for decision support in environmental scanning
Expert Systems with Applications: An International Journal
Combined Pattern Mining: From Learned Rules to Actionable Knowledge
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Expert Systems with Applications: An International Journal
Discovering interesting holes in data
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Discovering competitive intelligence by mining changes in patent trends
Expert Systems with Applications: An International Journal
Detection of the customer time-variant pattern for improving recommender systems
Expert Systems with Applications: An International Journal
Measuring similarity in feature space of knowledge entailed by two separate rule sets
Knowledge-Based Systems
The outer impartation information content of rules and rule sets
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A novel hybrid approach for interestingness analysis of classification rules
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
WebUser: mining unexpected web usage
International Journal of Business Intelligence and Data Mining
Conceptual distance for association rules post-processing
MEDI'11 Proceedings of the First international conference on Model and data engineering
Finding interesting rules exploiting rough memberships
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Modeling interestingness of streaming classification rules as a classification problem
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
A unified approach for discovery of interesting association rules in medical databases
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
A methodological view on knowledge-intensive subgroup discovery
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Knowledge discovery interestingness measures based on unexpectedness
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Rule induction research implicitly assumes that after producing the rules from a dataset, these rules will be used directly by an expert system or a human user. In real-life applications, the situation may not be as simple as that, particularly, when the user of the rules is a human being. The human user almost always has some previous concepts or knowledge about the domain represented by the dataset. Naturally, he/she wishes to know how the new rules compare with his/her existing knowledge. In dynamic domains where the rules may change over time, it is important to know what the changes are. These aspects of research have largely been ignored in the past. With the increasing use of machine leaming tcclmiques in practical applications such as data mining, this issue of post analysis of rules warrants greater emphasis and attention. In this paper, we propose a technique to deal with this problem. A system has been implemented to perform the post analysis of classification rules genemted by systems such as C4.5. The proposed technique is general and highly interactive. It will be particularly useful in data mining and data analysis.