Sequential Hierarchical Pattern Clustering
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
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In this paper, we introduce a novel, non-linear, fast, online algorithm for learning on large data sets. This algorithm, which we call Voted Spheres (VS) is a combination of hypersphere-fitting, and the idea of voting. The algorithm builds hyperspheres around points, with different hyperspheres belonging to different classes allowed to overlap. The advantages of the algorithm are that it is simple to implement, very efficient, and generalises well while being able to handle millions of data points. For the KDD intrusion detection data set consisting of 494,020 data points, the linear version of the algorithm requires under a minute on a standard desktop PC and achieves state of the art performance.