Subspace Memory Clustering

We present a new subspace clustering method called SuMC (Subspace Memory Clustering), which allows to efficiently divide a dataset $D \subset \bbbR^N$ into $k \in \bbbN$ pairwise disjoint clusters of possibly different dimensions. Since our approach is based on the memory compression, we do not need to explicitly specify dimensions of groups: in fact we only need to specify the mean number of scalars which is used to describe a data-point. In the case of one cluster our method reduces to a classical Karhunen-Loeve (PCA) transform. We test our method on some typical data from UCI repository and on data coming from real-life experiments.