C4.5: programs for machine learning
C4.5: programs for machine learning
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
The String-to-String Correction Problem
Journal of the ACM (JACM)
Introduction to the Special Section on Graph Algorithms in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
News Sensitive Stock Trend Prediction
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
Graph-Based Representations in Pattern Recognition: 5th IAPR International Workshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings (Lecture Notes in Computer Science)
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
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Algorithms for the analysis of graph sequences are proposed in this paper. In particular, we study the problem of recovering missing information and predicting the occurrence of nodes and edges in time series of graphs. Two different recovery schemes are developed. The first scheme uses reference patterns that are extracted from a training set of graph sequences, while the second method is based on decision tree induction. Our work is motivated by applications in computer network analysis. However, the proposed recovery and prediction schemes are generic and can be applied in other domains as well.