A review of robust clustering methods. (English) Zbl 1284.62375

Summary: Deviations from theoretical assumptions together with the presence of certain amount of outlying observations are common in many practical statistical applications. This is also the case when applying cluster analysis methods, where those troubles could lead to unsatisfactory clustering results. Robust clustering methods are aimed at avoiding these unsatisfactory results. Moreover, there exist certain connections between robust procedures and cluster analysis that make robust clustering an appealing unifying framework. A review of different robust clustering approaches in the literature is presented. Special attention is paid to methods based on trimming which try to discard most outlying data when carrying out the clustering process.


62H30 Classification and discrimination; cluster analysis (statistical aspects)
62G35 Nonparametric robustness
68T05 Learning and adaptive systems in artificial intelligence


clusfind; Flury
Full Text: DOI


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