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
A new data clustering approach: Generalized cellular automata
Information Systems
| Hi-index | 0.00 |
This paper introduces a new scalable approach to clusteringbased on synchronization of pulse-coupled oscillators. Eachdata point is represented by an integrate-and-fire oscillator, and the interaction between oscillators is defined according to the relative similarity between the points. The set of oscillators will self-organize into stable phase-locked subgroups. Our approach proceeds by loading only a subset of the data and allowing it to self-organize. Groups ofsynchronized oscillators are then summarized and purged from memory. We show that our method is robust, scales linearly, and can determine the number of clusters. The proposedapproach is empirically evaluated with several synthetic data sets and is used to segment large color images.