Handling Noise and Outliers in Fuzzy Clustering

摘要: Since it is an unsupervised data analysis approach, clustering relies solely on the location of points in space or, alternatively, their relative distances or similarities. As a consequence, can suffer from presence noisy and outliers, which obscure structure clusters thus may drive algorithms to yield suboptimal even misleading results. Fuzzy no exception this respect, although features aspect robustness, due outliers generally that are atypical for have lesser influence cluster parameters. Starting aspect, we provide paper overview different approaches with fuzzy be made less sensitive noise categorize them according component standard they modify.