BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Testing and spot-checking of data streams (extended abstract)
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
On computing correlated aggregates over continual data streams
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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering decision rules from numerical data streams
Proceedings of the 2004 ACM symposium on Applied computing
On demand classification of 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
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Lacking Labels in the Stream: Classifying Evolving Stream Data with Few Labels
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Multivariable stream data classification using motifs and their temporal relations
Information Sciences: an International Journal
Discovering event evolution graphs from news corpora
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The inverse classification problem
Journal of Computer Science and Technology
Cloud-based malware detection for evolving data streams
ACM Transactions on Management Information Systems (TMIS)
Concurrent semi-supervised learning of data streams
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Fuzzy based privacy preserving classification of data streams
Proceedings of the CUBE International Information Technology Conference
Data stream classification with artificial endocrine system
Applied Intelligence
International Journal of Data Warehousing and Mining
Automated Anomaly Detector Adaptation using Adaptive Threshold Tuning
ACM Transactions on Information and System Security (TISSEC)
An adaptive ensemble classifier for mining concept drifting data streams
Expert Systems with Applications: An International Journal
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Current models of the classification problem do not effectively handle bursts of particular classes coming in at different times. In fact, the current model of the classification problem simply concentrates on methods for one-pass classification modeling of very large data sets. Our model for data stream classification views the data stream classification problem from the point of view of a dynamic approach in which simultaneous training and test streams are used for dynamic classification of data sets. This model reflects real-life situations effectively, since it is desirable to classify test streams in real time over an evolving training and test stream. The aim here is to create a classification system in which the training model can adapt quickly to the changes of the underlying data stream. In order to achieve this goal, we propose an on-demand classification process which can dynamically select the appropriate window of past training data to build the classifier. The empirical results indicate that the system maintains a high classification accuracy in an evolving data stream, while providing an efficient solution to the classification task.