Explanations, machine learning, and creativity
Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
Decision Support Systems and Intelligent Systems
Decision Support Systems and Intelligent Systems
An Explanation Facility for Today's Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
IEEE Expert: Intelligent Systems and Their Applications
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Explanation oriented association mining using rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A Profit-Based Business Model for Evaluating Rule Interestingness
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Finding Explanations for Assisting Pattern Interpretation
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Re-mining positive and negative association mining results
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Re-mining item associations: Methodology and a case study in apparel retailing
Decision Support Systems
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We propose a new framework of explanation-oriented data mining by adding an explanation construction and evaluation phase to the data mining process. While traditional approaches concentrate on mining algorithms, we focus on explaining mined results. The mining task can be viewed as unsupervised learning that searches for interesting patterns. The construction and evaluation of mined patterns can be formulated as supervised learning that builds explanations. The proposed framework is therefore a simple combination of unsupervised learning and supervised learning. The basic ideas are illustrated using association mining. The notion of conditional association is used to represent plausible explanations of an association. The condition in a conditional association explicitly expresses the plausible explanations of an association.