A clustering algorithm by self-updating process

Details A clustering algorithm by self-updating process A clustering algorithm by self-updating process

2012-01-10

arxiv.org/abs/1201.1979v1

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We propose a simple and intuitive algorithm for clustering analysis. This algorithm stands from the viewpoint of elements to be clustered, and simulates the process of how they perform self-clustering. At the end of the process, elements belong to the same cluster converge to the same position, which represents the cluster's location in a p-dimensional space. The algorithm also manages to isolate noise, therefore is able to produce satisfactory clustering results even when the level of noise is high enough to obscure or distort the underlying patterns in the data. Our simulation study showed promising results compared to other clustering methods. Examples of gene expression data and image segmentation are presented

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