  Gaussian Mixture Models (GMM) have found many applications in density
estimation and data clustering. However, the model does not adapt well to
curved and strongly nonlinear data. Recently there appeared an improvement
called AcaGMM (Active curve axis Gaussian Mixture Model), which fits Gaussians
along curves using an EM-like (Expectation Maximization) approach.
  Using the ideas standing behind AcaGMM, we build an alternative active
function model of clustering, which has some advantages over AcaGMM. In
particular it is naturally defined in arbitrary dimensions and enables an easy
adaptation to clustering of complicated datasets along the predefined family of
functions. Moreover, it does not need external methods to determine the number
of clusters as it automatically reduces the number of groups on-line.
