Robust clustering

Historical and recent developments in the field of robust clustering and their applications are reviewed. The discussion focuses on different strategies that have been developed to reduce the sensitivity of clustering methods to outliers in data, while pointing out the importance of the need for both efficient partitioning and simultaneous robust model fitting. Although all clustering methods and algorithms have good partitioning capabilities when data are clean and free of outliers, they break down in the presence of outliers in the data. This is because classical development in the field of clustering has focused on such assumptions that data is free of noise and the data are well distributed, Robust model fitting, while retaining the partitioning power, involves the development of methods and algorithms that reject these classical assumptions either by explicitly incorporating robust statistical methods (often regression based) or by recasting the clustering problem in a way that does so implicitly. In this review, the robust model fitting aspect is identified in pertinent methodological and algorithmic advances and tied to related developments in robust statistics wherever possible. The paper also includes representative samples of various applications of robust clustering methods to both synthetic and real-world datasets.