International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Parallel depth first search. Part I. implementation
International Journal of Parallel Programming
Parallel depth first search. Part II. analysis
International Journal of Parallel Programming
Maximizing the predictive value of production rules
Artificial Intelligence
Proceedings of the sixth international workshop on Machine learning
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
ARIEL: a massively parallel symbolic learning assistant for protein structure and function
Artificial intelligence at MIT expanding frontiers
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning decision lists using homogeneous rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Communications of the ACM
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Scaling up inductive learning with massive parallelism
Machine Learning
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Error reduction through learning multiple descriptions
Machine Learning
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple Comparisons in Induction Algorithms
Machine Learning
Data Mining and Knowledge Discovery
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
The Effects of Training Set Size on Decision Tree Complexity
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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
Parallel Classification for Data Mining on Shared-Memory Multiprocessors
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Scalable data mining for rules
Scalable data mining for rules
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Generating C4.5 production rules in parallel
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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One of the defining challenges for the KDD research community is scaling up data mining algorithms to mine very large collections of data. This article summarizes, categorizes, and compares existing work on scaling up data mining algorithms. In order to provide focus and specific details, we concentrate on algorithms that build decision trees and rule sets; the issues and techniques generalize to other types of data mining. We discuss the important issues related to scaling up and highlight similarities among scaling techniques by categorizing them into three main approaches. We describe in detail the characteristic features of each category, using specific examples as needed, and we compare and contrast different constituent techniques.