C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Learning Under Persistent Drift
EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Mining Customer Change Model Based on Swarm Intelligence
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Mining dynamic association rules with comments
Knowledge and Information Systems
Constrained logistic regression for discriminative pattern mining
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Difference detection between two contrast sets
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Mining bridging rules between conceptual clusters
Applied Intelligence
An integrated approach for mining meta-rules
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Visualization of cluster changes by comparing self-organizing maps
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Much of the data mining research has been focused on devising techniques to build accurate models and to discover rules from databases. Relatively little attention has been paid to mining changes in databases collected over time. For businesses, knowing what is changing and how it has changed is of crucial importance because it allows businesses to provide the right products and services to suit the changing market needs. If undesirable changes are detected, remedial measures need to be implemented to stop or to delay such changes. In many applications, mining for changes can be more important than producing accurate models for prediction. A model, no matter how accurate, can only predict based on patterns mined in the old data. That is, a model requires a stable environment, otherwise it will cease to be accurate. However, in many business situations, constant human intervention (i.e., actions) to the environment is a fact of life. In such an environment, building a predictive model is of limited use. Change mining becomes important for understanding the behaviors of customers. In this paper, we study change mining in the contexts of decision tree classification for real-life applications.