It is shown for the first time in this paper, that Kleinberg’s ( 2002 ) (self-contradictory) axiomatic system for distance-based clustering fails (that is one of the data transforming axioms, consistency axiom, turns out to be identity transformation) in fixed-dimensional Euclidean space due to the consistency axiom limitations and that its replacement with inner-consistency or outer consistency does not help if continuous data transformations are required. Therefore we formulate a new, sound axiomatic framework for cluster analysis in the fixed dimensional Euclidean space, suitable for k -means like algorithms. The system incorporates centric consistency axiom and motion consistency axiom which induce clustering preserving transformations useful e.g. for deriving new labelled sets for testing clustering procedures. It is suitable for continuous data transformations so that labelled data with small perturbations can be derived. Unlike Kleinberg’s consistency, the new axioms do not lead the data outside of Euclidean space nor cause increase in data dimensionality. Our cluster preserving transformations have linear complexity in data transformation and checking. They are in practice less restrictive, less rigid than Kleinberg’s consistency as they do not enforce inter-cluster distance increase and inner cluster distance decrease when performing clustering preserving transformation.