C4.5: programs for machine learning
C4.5: programs for machine learning
IEEE Transactions on Knowledge and Data Engineering
News Sensitive Stock Trend Prediction
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Optimizing Similarity Search for Arbitrary Length Time Series Queries
IEEE Transactions on Knowledge and Data Engineering
Data Mining in Time Series Database
Data Mining in Time Series Database
Graph-based tools for data mining and machine learning
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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This paper is concerned with time series of graphs and compares two novel schemes that are able to predict the presence or absence of nodes in a graph. Our work is motivated by applications in computer network monitoring. However, the proposed prediction methods are generic and can be used in other applications as well. Experimental results with graphs derived from real computer networks indicate that a correct prediction rate of up to 97% can be achieved.