Algorithms for clustering data
Algorithms for clustering data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
ACM Transactions on Knowledge Discovery from Data (TKDD)
Scalable community discovery on textual data with relations
Proceedings of the 17th ACM conference on Information and knowledge management
Scalable graph clustering using stochastic flows: applications to community discovery
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Clustering Large Attributed Graphs: An Efficient Incremental Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Computer Science Review
Spectral K-way ratio-cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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In this paper, we present different combined clustering methods and we evaluate their performances and their results on a dataset with ground truth. This dataset, built from several sources, contains a scientific social network in which textual data is associated to each vertex and the classes are known. Indeed, while the clustering task is widely studied both in graph clustering and in non supervised learning, combined clustering which exploits simultaneously the relationships between the vertices and attributes describing them, is quite new. We argue that, depending on the kind of data we have and the type of results we want, the choice of the clustering method is important and we present some concrete examples for underlining this.