Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Knowledge and Data Engineering
Analysis of Biological Networks (Wiley Series in Bioinformatics)
Analysis of Biological Networks (Wiley Series in Bioinformatics)
Bootstrapping the interactome: unsupervised identification of protein complexes in yeast
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Self-adjust local connectivity analysis for spectral clustering
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
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In this paper, we study spectral clustering for detecting protein complexes in PPI (protein-protein interaction) networks, focusing on two open issues: (i) constructing similarity graphs; and (ii) determining the number of clusters. First, we study four similarity graphs to construct graph Laplacian matrices. Then we propose a method to determine the number of clusters based on the properties of PPI networks. Experimental results on PPI networks from DIP data and MIPS data indicate that each similarity graph shows its strengths and disadvantages, and our finding of the number of clusters improves the clustering quality. Finally, spectral clustering obtains results in detecting protein complexes that are comparable to those obtained from several other typical algorithms.