Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
The Strength of Weak Learnability
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A clustering method based on boosting
Pattern Recognition Letters
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Clustering graphs by weighted substructure mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Mining Large Networks with Subgraph Counting
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Analysis of Network Data: Methods and Models
Statistical Analysis of Network Data: Methods and Models
SemiBoost: Boosting for Semi-Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Science Review
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The division of a water supply network (WSN) into isolated supply clusters aims at improving the management of the whole system. This paper deals with the application of spectral clustering to achieve this aim. A semi-supervised approach can take into account the graph structure of a network and incorporate the corresponding hydraulic constraints and the other available vector information from the WSN. Several of the disadvantages of these methodologies stem from the largeness of the most WSN and the associated computational complexity. To solve these problems, we propose subsampling graph data to run successive weak clusters and build a single robust cluster configuration. The resulting methodology has been tested in a real network and can be used to successfully partition large WSNs.