๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Fuzzy clustering method based on perturbation

โœ Scribed by Qing He; Hong-Xing Li; Zhong-Zhi Shi; E.S. Lee


Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
1015 KB
Volume
46
Category
Article
ISSN
0898-1221

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Gaussian clustering method based on maxi
โœ Rui-Ping Li; Masao Mukaidono ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 390 KB

A new method of fuzzy clustering is proposed. This is a complete Gaussian membership function derived by means of the maximum-entropy interpretation. Compared to the traditional fuzzy c-means (FCM) method, our approach exhibits the following two advantages: (1) having clearer physical meaning and we

Investigations on fuzzy thresholding bas
โœ C.V Jawahar; P.K Biswas; A.K Ray ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 715 KB

Thresholding, the problem of pixel classification is attempted here using fuzzy clustering algorithms. The segmented regions are fuzzy subsets, with soft partitions characterizing the region boundaries. The validity of the assumptions and thresholding schemes are investigated in the presence of dist

A stability based validity method for fu
โœ M. Falasconi; A. Gutierrez; M. Pardo; G. Sberveglieri; S. Marco ๐Ÿ“‚ Article ๐Ÿ“… 2010 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 657 KB

An important goal in cluster analysis is the internal validation of results using an objective criterion. Of particular relevance in this respect is the estimation of the optimum number of clusters capturing the intrinsic structure of your data. This paper proposes a method to determine this optimum

Entropy-based fuzzy clustering and fuzzy
โœ J Yao; M Dash; S.T Tan; H Liu ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 657 KB

Fuzzy clustering is capable of finding vague boundaries that crisp clustering fails to obtain. But time complexity of fuzzy clustering is usually high, and the need to specify complicated parameters hinders its use. In this paper, an entropy-based fuzzy clustering method is proposed. It automaticall

A Coarse to Fine 3D Registration Method
โœ Jean-Philippe Tarel; Nozha Boujemaa ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 380 KB

An important problem in computer vision is to determine how features extracted from images are connected to an existing model. In this paper, we focus on solving the registration problem, i.e., obtaining Euclidean transformation parameters between several 3D data sets, whether partial or exhaustive.