The
Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated
density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put
into the same cluster. A disadvantage of
Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginning and never converges exactly to the
maximum, it just comes close.
We introduce a new hill climbing procedure for Gaussian kernels, which adjusts the step size automatically at no extra costs.
We prove that the procedure converges exactly towards a local maximum by reducing it to a special case of the expectation
maximization algorithm. We show experimentally that the new procedure needs much less iterations and can be accelerated by
sampling based methods with sacrificing only a small amount of accuracy.