Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Online Association Rule Mining
Online Association Rule Mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data streams classification by incremental rule learning with parameterized generalization
Proceedings of the 2006 ACM symposium on Applied computing
Spatiotemporal Relational Probability Trees: An Introduction
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Combining Multiple Interrelated Streams for Incremental Clustering
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Stream Clustering of Growing Objects
DS '09 Proceedings of the 12th International Conference on Discovery Science
Tree induction over perennial objects
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Incremental classification rules based on association rules using formal concept analysis
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Modeling interestingness of streaming classification rules as a classification problem
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
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We study classification over a slow stream of complex objects like customers or students. The learning task must take into account that an object's label is influenced by incoming data from adjoint, fast streams of transactions, e.g. customer purchases or student exams, and that this label may even change over time. This task involves combining the streams, and exploiting associations between the target label and attribute values in the fast streams. We propose a method for the discovery of classification rules over such a confederation of streams, and we use it to enhance a decision tree classifier. We show that the new approach has competitive predictive power while building much smaller decision trees than the original classifier.