SyMP: An Efficient Clustering Approach to Identify Clusters of Arbitrary Shapes in Large Data Sets

摘要

We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscillator and uses the relative similarity between the points to model the interaction between the oscillators. SyMP is robust to noise and outliers, determines the number of clusters in an unsupervised manner, identifies clusters of arbitrary shapes, and can handle very large data sets. The robustness of SyMP is an intrinsic property of the synchronization mechanism. To determine the optimum number of clusters, SyMP uses a dynamic resolution parameter. To identify clusters of various shapes, SyMP models each cluster by multiple Gaussian components. The number of components is automatically determined using a dynamic intra-cluster resolution parameter. Clusters with simple shapes would be modeled by few components while clusters with more complex shapes would require a larger number of components. The scalable version of SyMP uses an efficient incremental approach that requires a simple pass through the data set. The proposed clustering approach is empirically evaluated with several synthetic and real data sets, and its performance is compared with CURE.