Space-efficient online computation of quantile summaries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Continuous queries over data streams
ACM SIGMOD Record
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Is random model better? On its accuracy and efficiency
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On the optimality of probability estimation by random decision trees
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking concept drifting with an online-optimized incremental learning framework
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
An active learning system for mining time-changing data streams
Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streams
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining Concept-Drifting Data Streams with Multiple Semi-Random Decision Trees
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Ensembles in adversarial classification for spam
Proceedings of the 18th ACM conference on Information and knowledge management
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Communication-Efficient Privacy-Preserving Clustering
Transactions on Data Privacy
FAW'07 Proceedings of the 1st annual international conference on Frontiers in algorithmics
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Unsupervised change analysis using supervised learning
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Effective sentiment stream analysis with self-augmenting training and demand-driven projection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Random ensemble decision trees for learning concept-drifting data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Resource adaptive periodicity estimation of streaming data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
A new method of mining data streams using harmony search
Journal of Intelligent Information Systems
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We demonstrate StreamMiner, a random decision-tree ensemble based engine to mine data streams. A fundamental challenge in data stream mining applications (e.g., credit card transaction authorization, security buy-sell transaction, and phone call records, etc) is concept-drift or the discrepancy between the previously learned model and the true model in the new data. The basic problem is the ability to judiciously select data and adapt the old model to accurately match the changed concept of the data stream. StreamMiner uses several techniques to support mining over data streams with possible concept-drifts. We demonstrate the following two key functionalities of StreamMiner: 1. Detecting possible concept-drift on the fly when the trained streaming model is used to classify incoming data streams without knowing the ground truth. 2. Systematic data selection of old data and new data chunks to compute the optimal model that best fits on the changing data streams.