Challenge of the week: Piecewise linear clustering versus SVM

In this challenge, we ask you to invent a new technique for clustering, based on separating hyperplanes. SVM (support vector machines) add many fictitious (dummy) variables and a non-linear mapping (to increase dimensionality and find hyperplanes on transformed variables), thus providing nearly or exact class separation (the purpose of clustering!) when traditional linear clustering fails.