Optimization of distributed tree queries
Journal of Computer and System Sciences
A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
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SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Intelligent Query Answering by Knowledge Discovery Techniques
IEEE Transactions on Knowledge and Data Engineering
On the Complexity of Distributed Query Optimization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
High performance data mining (tutorial PM-3)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis and synthesis of agents that learn from distributed dynamic data sources
Emergent neural computational architectures based on neuroscience
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Parallel and Distributed Data Mining: An Introduction
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
International Journal of Hybrid Intelligent Systems
Decomposable algorithms for nearest neighbor computing
Journal of Parallel and Distributed Computing
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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Most algorithms for learning and pattern discovery in data assume that all the needed data is available on one computer at a single site. This assumption does not hold in situations where a number of independent databases reside on geographically distributed nodes of a computer network. These databases cannot be moved to a single site due to size, security, privacy and data-ownership concerns but all of them together constitute the dataset in which patterns must be discovered. Some pattern discovery algorithms can be adapted to such situations and some others become inefficient or inapplicable. In this paper we show how a decision-tree induction algorithm may be adapted for distributed data situations. We also discuss some general issues relating to the adaptability of other pattern discovery algorithms to distributed data situations