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Semi-Supervised visual clustering for spherical coordinates systems

Boris Chidlovskii, Loïc Lecerf
In this paper we propose a method that combines the advanced data analysis of the automatic statistical methods and the flexibility and manual parameter tuning of interactive visual clustering. We present the Semi-supervised visual clustering (SSVC) interface; its main contribution is the learning of the optimal projection distance metric for the star and spherical coordinate visualization systems. Beyond the conventional manual setting, it couples the visual clustering with the automatic setting where the projection distance metric is learned from the available set of user feedbacks in the form of either item similarities/dissimilarities or direct item annotations. Moreover, SSVC interface allows for the hybrid setting where some parameters are manually set by the user while the remaining parameters are determined by the optimal distance algorithm.
The 23rd Annual ACM Symposium on Applied Computing, Fortaleza, Brazil, March 16-20, 2008.
Full paper available on ACM Portal