Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Introduction to the theory of neural computation
Introduction to the theory of neural computation
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Evaluation of adaptive mixtures of competing experts
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural Computation
An Introduction to Computer Simulation Methods: Applications to Physical Systems (3rd Edition)
An Introduction to Computer Simulation Methods: Applications to Physical Systems (3rd Edition)
Hierarchical, unsupervised learning with growing via phase transitions
Neural Computation
The Journal of Machine Learning Research
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
Mean shift spectral clustering
Pattern Recognition
Kernel bandwidth estimation for nonparametric modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Detecting communities in sparse MANETs
IEEE/ACM Transactions on Networking (TON)
| Hi-index | 0.00 |
We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures, it is completely ordered; all spins are aligned. At very high temperatures, the system does not exhibit any ordering, and in an intermediate regime, clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.