BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
ACM SIGMOD Record
Feature Selection for Building Cost-Effective Data Stream Classifiers
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
A Framework for On-Demand Classification of Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Adaptive non-linear clustering in data streams
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
On string classification in data streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining correlated bursty topic patterns from coordinated text streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Using classifier ensembles to label spatially disjoint data
Information Fusion
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
An active learning system for mining time-changing data streams
Intelligent Data Analysis
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
Distributed mining of censored production rules in data streams: an evolutionary approach
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
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
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
CBDT: A Concept Based Approach to Data Stream Mining
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A framework for flexible clustering of multiple evolving data streams
International Journal of Advanced Intelligence Paradigms
Measuring evolving data streams' behavior through their intrinsic dimension
New Generation Computing
Clustering over Evolving Data Streams Based on Online Recent-Biased Approximation
Knowledge Acquisition: Approaches, Algorithms and Applications
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
History Guided Low-Cost Change Detection in Streams
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
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
Event-based lossy compression for effective and efficient OLAP over data streams
Data & Knowledge Engineering
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A framework to enforce access control over data streams
ACM Transactions on Information and System Security (TISSEC)
Similarity search and locality sensitive hashing using ternary content addressable memories
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Data compression by volume prototypes for streaming data
Pattern Recognition
λ-Perceptron: An adaptive classifier for data streams
Pattern Recognition
Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases
Artificial Intelligence in Medicine
Partial drift detection using a rule induction framework
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Detecting and ordering salient regions
Data Mining and Knowledge Discovery
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
Discovering customer intent in real-time for streamlining service desk conversations
Proceedings of the 20th ACM international conference on Information and knowledge management
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
A scalable distributed stream mining system for highway traffic data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Discovery and diagnosis of behavioral transitions in patient event streams
ACM Transactions on Management Information Systems (TMIS)
Continuous trend-based classification of streaming time series
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
Multivariate stream data classification using simple text classifiers
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
On clustering techniques for change diagnosis in data streams
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Homogeneous and heterogeneous distributed classification for pocket data mining
Transactions on Large-Scale Data- and Knowledge-Centered Systems V
Non-linear data stream compression: foundations and theoretical results
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Statistical Analysis and Data Mining
Learning deep belief networks from non-stationary streams
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Real time processing of data from patient biodevices
HIKM '11 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 120
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
Stream mining on univariate uncertain data
Applied Intelligence
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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 testing 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.