Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synchronization of pulse-coupled biological oscillators
SIAM Journal on Applied Mathematics
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation based on oscillatory correlation
Neural Computation
Collective excitation phenomena and their applications
Pulsed neural networks
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by competitive agglomeration
Pattern Recognition
A comparison of fuzzy shell-clustering methods for the detection of ellipses
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Physiologically motivated image fusion for object detection using a pulse coupled neural network
IEEE Transactions on Neural Networks
Range image segmentation using a relaxation oscillator network
IEEE Transactions on Neural Networks
Clustering within Integrate-and-Fire Neurons for Image Segmentation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Phase Transition and Hysteresis in an Ensemble of Stochastic Spiking Neurons
Neural Computation
Preprocessing enhancements to improve data mining algorithms
International Journal of Business Intelligence and Data Mining
A synchronization metric for meshed networks of pulse-coupled oscillators
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
A network of integrate and fire neurons for visual selection
Neurocomputing
A novel clustering algorithm based upon games on evolving network
Expert Systems with Applications: An International Journal
A self-organized network for data clustering
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Self-organizing synchronization with inhibitory-coupled oscillators: Convergence and robustness
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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We introduce an efficient synchronization model that organizes a population of Integrate and Fire oscillators into stable and structured groups. Each oscillator fires synchronously with all the others within its group, but the groups themselves fire with a constant phase difference. The structure of the synchronized groups depends on the choice of the coupling function. We show that by defining the interaction between oscillators according to the relative distance between them, our model can be used as a general clustering algorithm. Unlike existing models, our model incorporates techniques from relational and prototype-based clustering methods and results in a clustering algorithm that is simple, efficient, robust, unbiased by the size of the clusters, and that can find an arbitrary number of clusters. In addition to helping the model self-organize into stable groups, the synergy between clustering and synchronization reduces the computational complexity significantly. The resulting clustering algorithm has several advantages over conventional clustering techniques. In particular, it can generate a nested sequence of partitions and it can determine the optimum number of clusters in an efficient manner. Moreover, since our approach does not involve optimizing an objective function, it is not sensitive to initialization and it can incorporate nonmetric similarity measures. We illustrate the performance of our algorithms with several synthetic and real data sets.