An adaptive granularity clustering method based on hyper-ball
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The purpose of cluster analysis is to classify elements according to their similarity. Its applications range from astronomy to bioinformatics and pattern recognition. Our method is based on the idea that the data with similar distribution form a hyper-ball and the adjacent hyper-balls form a cluster. Based on the cognitive law of "large scale first", this method can identify clusters without considering shape in a simple and non-parametric way. Experimental results on several datasets demonstrate the effectiveness of the algorithm.
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