Notice of Violation of IEEE Publication Principles: An Implementation of Fuzzy Clustering with Size and Shape Constraints

Notice of Violation of IEEE Publication Principles

"An Implementation of Fuzzy Clustering with Size and Shape Constraints,"
Wang, Yan Jiang, and Hai-long Zhang,
in the Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp.922-925

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper is a near verbatim copy of the paper cited below. The original text was copied without attribution.

"Fuzzy and Probabilistic Clustering with Shape and Size Constraints"
by Christian Borgelt and Rudolf Kruse,
in the Proceedings of the 11th International Fuzzy Systems Association World Congress (IFSA'05, Beijing, China), 945-950. Tsinghua University Press and Springer-Verlag, Beijing, China, and Heidelberg, Germany 2005
It is suggested that a shape regularization method as well as methods to constrain the cluster size and weight for clustering algorithms. The basic idea is to introduce a tendency towards equal length of the major axes of the represented ellipsoid and towards equal cluster sizes. Experiments show, these methods improve the robustness of the more sophisticated fuzzy clustering algorithms, which without them suffer from instabilities even on fairly simple data sets. Regularized and constrained clustering is so robust that it can even be used without an initialization by the fuzzy c-means algorithm.