Algorithms for clustering data
Algorithms for clustering data
A new approach to the minimum cut problem
Journal of the ACM (JACM)
Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification
Advances in Data Analysis and Classification
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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Path Based Clustering assigns two objects to the same cluster if they are connected by a path with high similarity between adjacent objects on the path. In this paper, we propose a fast agglomerative algorithm to minimize the Path Based Clustering cost function. To enhance the reliability of the clustering results a stochastic resampling method is used to generate candidate solutions which are merged to yield empirical assignment probabilities of objects to clusters. The resampling algorithm measures the reliability of the clustering solution and, based on their stability, determines the number of clusters.