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Robust clustering techniques are an effective way of clustering data in such a form that effect of outliers on the data clusters is minimized. In this book six data clustering techniques are reviewed and analysed based upon the robust characteristics of clustering. Six data clustering algorithms namely: Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), Possibilistic Fuzzy C-means (PFCM), Credibilistic Fuzzy C-means (CFCM), Noise Clustering (NC) and Density Oriented Fuzzy C-Means (DOFCM) are analysed based upon synthetic noisy data-sets and standard data-sets like DUNN and Bensaid.