  Mean Shift today, is widely used for mode detection and clustering. The
technique though, is challenged in practice due to assumptions of isotropicity
and homoscedasticity. We present an adaptive Mean Shift methodology that allows
for full anisotropic clustering, through unsupervised local bandwidth
selection. The bandwidth matrices evolve naturally, adapting locally through
agglomeration, and in turn guiding further agglomeration. The online
methodology is practical and effecive for low-dimensional feature spaces,
preserving better detail and clustering salience. Additionally, conventional
Mean Shift either critically depends on a per instance choice of bandwidth, or
relies on offline methods which are inflexible and/or again data instance
specific. The presented approach, due to its adaptive design, also alleviates
this issue - with a default form performing generally well. The methodology
though, allows for effective tuning of results.
