Abstract
This paper describes a family of methods of
fuzzy clustering handling objects with weights.
Weighted objects easily appear when an individual
is a representative of several data units.
Fuzzy c-means and possibilistic
clustering algorithms for weighted objects are proposed.
Relationships as well as differences between solutions
of possibilistic and fuzzy c-means methods are described.
Moreover a kernelized fuzzy c-means algorithm
with weighted objects is studied.
Numerical examples show effectiveness of
the proposed methods.
