An efficient method for computing leading eigenvalues and eigenvectors of large asymmetric matrices
Journal of Scientific Computing
Character recognition—a review
Pattern Recognition
ACM Computing Surveys (CSUR)
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cut and Image Segmentation
Normalized Cut and Image Segmentation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Large-Scale Clustering through Functional Embedding
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Stochastic Competitive Learning Applied to Handwritten Digit and Letter Clustering
SIBGRAPI '11 Proceedings of the 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images
Uncovering overlapping cluster structures via stochastic competitive learning
Information Sciences: an International Journal
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In this paper, we study a new type of competitive learning scheme realized on large-scale networks. The model consists of several agents walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder agents. In the end of the process, each agent dominates a community (a strongly connected subnetwork). Here, the model is described by a stochastic dynamical system. In this paper, a mathematical analysis for uncovering the system's properties is presented. In addition, the model is applied to solve handwritten digits and letters clustering problems. An interesting feature is that the model is able to group the same digits or letters even with considerable distortions into the same cluster. Computer simulations reveal that the proposed technique presents high precision of cluster detections, as well as low computational complexity.