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A Robust Fuzzy Clustering Approach And Its Application To Principal Component Analysis
2010
Intelligent Automation and Soft Computing
A robust fuzzy clustering approach is proposed to simplify the task of principal component analysis (PCA) by reducing the data complexity of an image. This approach performs well on function curves and character images that not only have loops, sharp corners and intersections but also include data with noise and outliers. The proposed approach is composed of two phases: firstly, input data are clustered using the proposed distance analysis to get good and reasonable number of clusters;
doi:10.1080/10798587.2010.10643059
fatcat:rm7rlvxnprfsbh5jcreuimcaoi