document: SyMP : an efficient clustering approach to identify clusters of arbitrary shapes in large data sets 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 .
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