Improved covariance estimation for Gustafson-Kessel clustering

Babuka, R.; Veen, Peter J. van der; Kaymak, Uzay

doi:10.1109/fuzz.2002.1006654

Cited by 104 publications

(106 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Improved covariance estimation for Gustafson-Kessel algorithm has been introduced in [20]. The objective function of GK algorithm is described by:…”

Section: Gustafson-kessel Clustering Approach (Gk)mentioning

confidence: 99%

Dynamic Hysteresis Model Based on Fuzzy Clustering Approach

Mordjaoui¹,

Boudjema²

2012

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-Hysteretic behavior model of soft magnetic material usually used in electrical machines and electronic devices is necessary for numerical solution of Maxwell equation. In this study, a new dynamic hysteresis model is presented, based on the nonlinear dynamic system identification from measured data capabilities of fuzzy clustering algorithm. The developed model is based on a Gustafson-Kessel (GK) fuzzy approach used on a normalized gathered data from measured dynamic cycles on a C core transformer made of 0.33mm laminations of cold rolled SiFe. The number of fuzzy rules is optimized by some cluster validity measures like 'partition coefficient' and 'classification entropy'. The clustering results from the GK approach show that it is not only very accurate but also provides its effectiveness and potential for dynamic magnetic hysteresis modeling.

show abstract

“…Improved covariance estimation for Gustafson-Kessel algorithm has been introduced in [20]. The objective function of GK algorithm is described by:…”

Section: Gustafson-kessel Clustering Approach (Gk)mentioning

confidence: 99%

Dynamic Hysteresis Model Based on Fuzzy Clustering Approach

Mordjaoui¹,

Boudjema²

2012

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…where i A is the norm inducing matrix, which allows the distance norm to adapt to the local topological structure of the data (see (Babuska et al, 2002)). The GK algorithm iteratively optimizes the following objective function to derive ij and i A adapts the local topological structure of the cluster shape as follows:…”

Section: Appendix a The Gustafson-kessel (Gk) Algorithmmentioning

confidence: 99%

Detection and separation of generic‐shaped objects by fuzzy clustering

Ali

Karmakar

Dooley

2010

International Journal of Intelligent Computing and Cybernetics

View full text Add to dashboard Cite

Received ( ) Revised ( ) Accepted ( ) ABSTRACT Purpose -Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometricallyshaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily-shaped objects. Design/Methodology/Approach -With the aim of separating arbitrary shaped objects in an image, this paper presents a new detection and separation of generic shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling. Findings-Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects. Originality/value-The proposed FKG algorithm can be highly used in the applications where object segmentation is necessary. Like this algorithm can be applied in MPEG-4 for real object segmentation that is already applied in synthetic object segmentation.

show abstract

“…where i A is the norm inducing matrix that allows the distance norm to adapt to the local topological structure of the data [12], A is used to adapt to the local topological structure of the data:…”

Section: Shape-based Clustering Algorithmsmentioning

confidence: 99%

“…The GK algorithm is characterised by adapting automatically the local data distance metric to the shape of the cluster using a covariance matrix [7], [12], which is based on the iterative optimization of the following objective function:…”

Section: Shape-based Clustering Algorithmsmentioning

confidence: 99%