Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Path Based Pairwise Data Clustering with Application to Texture Segmentation
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Uniformity Testing Using Minimal Spanning Tree
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability-based validation of clustering solutions
Neural Computation
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Methods for Automatic Multiscale Data Clustering
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Weighted rank aggregation of cluster validation measures
Bioinformatics
Quality indices for (practical) clustering evaluation
Intelligent Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Testing for Uniformity in Multidimensional Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Clustering 100,000 Protein Structure Decoys in Minutes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Identification of Essential Proteins Based on Edge Clustering Coefficient
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Clustering validation indexes are intended to assess the goodness of clustering results. Many methods used to estimate the number of clusters rely on a validation index as a key element to find the correct answer. This paper presents a new validation index based on graph concepts, which has been designed to find arbitrary shaped clusters by exploiting the spatial layout of the patterns and their clustering label. This new clustering index is combined with a solid statistical detection framework, the gap statistic. The resulting method is able to find the right number of arbitrary-shaped clusters in diverse situations, as we show with examples where this information is available. A comparison with several relevant validation methods is carried out using artificial and gene expression data sets. The results are very encouraging, showing that the underlying structure in the data can be more accurately detected with the new clustering index. Our gene expression data results also indicate that this new index is stable under perturbation of the input data.