Abstract
In this chapter we discuss three modifications of previous algorithms which have been proposed in an attempt to compensate for the difficulties caused by variations in cluster shape. The basic dilemma is that “clusters” defined by criterion functions usually take mathematical substance via metrical distances in data space. Each metric induces its own unseen but quite pervasive topological structure on ℝp due to the geometric shape of the open balls it defines. This often forces the criterion function employing d to unwittingly favor clusters in X having this basic shape—even when none are present! In S21, we discuss a novel approach due to Backer which “inverts” several previous strategies. S22 considers an interesting modification of the FCM functional J m due to Gustafson and Kessel,(54) which uses a different norm for each cluster! S23 and S24 discuss generalization of the fuzzy c-means algorithms (A11.1) in a different way—the prototypes v i for J m (U, v) become r-dimensional linear varieties in ℝp, 0 ≤ r ≤ p − 1.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1981 Plenum Press, New York
About this chapter
Cite this chapter
Bezdek, J.C. (1981). Modified Objective Function Algorithms. In: Pattern Recognition with Fuzzy Objective Function Algorithms. Advanced Applications in Pattern Recognition. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-0450-1_5
Download citation
DOI: https://doi.org/10.1007/978-1-4757-0450-1_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-0452-5
Online ISBN: 978-1-4757-0450-1
eBook Packages: Springer Book Archive