Several novel and robust learning algorithms, with the aim to overcome the drawbacks of traditional clustering algorithms, are developed for data clustering and its applications. The effectiveness and superiority of the proposed methods are supported by experimental results. 1) Te proposed RDA exhibits several robust clustering characteristics: robust to the initialization; robust to cluster volumes; and robust to noise and outliers. 2) The proposed IFCSS algorithm achieves two robust clustering characteristics: the robustness against noisy points is obtained by the maximization of mutual...