BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 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
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
Characterizing memory requirements for queries over continuous data streams
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Incremental rule learning based on example nearness from numerical data streams
Proceedings of the 2005 ACM symposium on Applied computing
A Framework for On-Demand Classification of Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Data streams classification by incremental rule learning with parameterized generalization
Proceedings of the 2006 ACM symposium on Applied computing
Collaborative filtering on streaming data with interest-drifting
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
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 decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
OcVFDT: one-class very fast decision tree for one-class classification of data streams
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Multivariable stream data classification using motifs and their temporal relations
Information Sciences: an International Journal
Mining data streams with periodically changing distributions
Proceedings of the 18th ACM conference on Information and knowledge management
PLANET: massively parallel learning of tree ensembles with MapReduce
Proceedings of the VLDB Endowment
Association rule mining in multiple, multidimensional time series medical data
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Efficient decision tree construction for mining time-varying data streams
CASCON '09 Proceedings of the 2009 Conference of the Center for Advanced Studies on Collaborative Research
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
Maintaining optimal multi-way splits for numerical attributes in data streams
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Modeling of network computing systems for decision tree induction tasks
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Adaptive system anomaly prediction for large-scale hosting infrastructures
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
Efficient decision tree re-alignment for clustering time-changing data streams
From active data management to event-based systems and more
Classification model for data streams based on similarity
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Knowledge maintenance on data streams with concept drifting
CIS'04 Proceedings of the First international conference on Computational and Information Science
Improving the performance of data stream classifiers by mining recurring contexts
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Learning from data streams with only positive and unlabeled data
Journal of Intelligent Information Systems
The CART decision tree for mining data streams
Information Sciences: an International Journal
Building fast decision trees from large training sets
Intelligent Data Analysis
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Decision tree construction is a well studied problem in data mining. Recently, there has been much interest in mining streaming data. Domingos and Hulten have presented a one-pass algorithm for decision tree construction. Their work uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed.In this paper, we revisit this problem. We make the following two contributions: 1) We present a numerical interval pruning (NIP) approach for efficiently processing numerical attributes. Our results show an average of 39% reduction in execution times. 2) We exploit the properties of the gain function entropy (and gini) to reduce the sample size required for obtaining a given bound on the accuracy. Our experimental results show a 37% reduction in the number of data instances required.