Kernel Treelets
This method addresses clustering for general data types, but it appears incremental as it extends treelets with kernelization.
The paper tackles hierarchical clustering for non-numeric data by introducing kernel treelets (KT), which combine treelets with kernel methods to produce a multi-resolution basis in feature space, demonstrating effectiveness through examples.
A new method for hierarchical clustering is presented. It combines treelets, a particular multiscale decomposition of data, with a projection on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), effectively substitutes the correlation coefficient matrix used in treelets with a symmetric, positive semi-definite matrix efficiently constructed from a kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yield a multi-resolution sequence of basis on the data directly in feature space. The effectiveness and potential of KT in clustering analysis is illustrated with some examples.