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
Incremental Induction of Decision Trees
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Spatiotemporal Relational Probability Trees: An Introduction
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Breast cancer identification: KDD CUP winner's report
ACM SIGKDD Explorations Newsletter
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
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Regression on evolving multi-relational data streams
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
Classification rule mining for a stream of perennial objects
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Where are we going? predicting the evolution of individuals
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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We study the tree induction over a stream of perennial objects. The perennial objects are dynamic in nature and cannot be forgotten. The objects come from a multi-table stream, e.g., streams of Customer and Transaction. As the Transactions arrive, the perennial Customers' profiles grow and accumulate over time. To perform tree induction, we propose a tree induction algorithm that can handle perennial objects. The algorithm also encompasses a method that identifies and adapts to the concept drift in the stream. We have also incorporated a conventional classifier (kNN) at the leaves to further improve the classification accuracy of our algorithm. We have evaluated our method on a synthetic dataset and the PKDD Challenge 1999 dataset.