ECML '02 Proceedings of the 13th European Conference on 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
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Dependency Networks
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Shrinkage Approach for Modeling Non-stationary Relational Autocorrelation
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Improving learning in networked data by combining explicit and mined links
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting
DS '09 Proceedings of the 12th International Conference on Discovery Science
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Mining ranking models from dynamic network data
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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Regression inference in network data is a challenging task in machine learning and data mining. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the values of the response variable at a given node depend on the values of the variables (predictor and response) at the nodes connected to the given node. This phenomenon is a direct violation of the assumption of independent (i.i.d.) observations: At the same time, it offers a unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. In this paper, we propose a data mining method that explicitly considers autocorrelation when building regression models from network data. The method is based on the concept of predictive clustering trees (PCTs), which can be used both for clustering and predictive tasks: PCTs are decision trees viewed as hierarchies of clusters and provide symbolic descriptions of the clusters. In addition, PCTs can be used for multi-objective prediction problems, including multi-target regression and multi-target classification. Empirical results on real world problems of network regression show that the proposed extension of PCTs performs better than traditional decision tree induction when autocorrelation is present in the data.