Results 331 to 340 of about 948,152 (383)

Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering

Int. J. Medical Informatics, 2019
OBJECTIVE Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cancerous ...
Nudrat Nida, Aun Irtaza, A. Javed, M. Yousaf, M. Mahmood   +4 more
semanticscholar   +1 more source

Projected fuzzy C-means clustering with locality preservation

Pattern Recognition, 2020
Traditional partition-based clustering algorithms, hard or fuzzy version of C-means, could not deal with high-dimensional data sets effectively as redundant features may impact the computation of distances and local spatial structures among patterns are ...
Jie Zhou, W. Pedrycz, Xiaodong Yue, C. Gao, Zhihui Lai, Jun Wan   +5 more
semanticscholar   +1 more source

Fuzzy c-means for Fuzzy Hierarchical Clustering

The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., 2005
This paper describes an algorithm for building fuzzy hierarchies. These are hierarchies where the elements can have fuzzy membership to the nodes. The paper presents an approach that mainly follows a bottom-up strategy, and describes the functions needed to operate with fuzzy variables.
openaire   +1 more source

Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering

2015
The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers.
P. Gopala Krishna, D. Lalitha Bhaskari
openaire   +1 more source

Fuzzy C-Means in High Dimensional Spaces

International Journal of Fuzzy System Applications, 2011
High dimensions have a devastating effect on the FCM algorithm and similar algorithms. One effect is that the prototypes run into the centre of gravity of the entire data set. The objective function must have a local minimum in the centre of gravity that causes FCM’s behaviour. In this paper, examine this problem.
Winkler, Roland, Klawonn, Frank, Kruse, Rudolf   +2 more
openaire   +1 more source

An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

Medical Hypotheses, 2019
Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MRI ...
Fatih Özyurt, E. Sert, Derya Avcı
semanticscholar   +1 more source

Fuzzy c-means clustering of incomplete data

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2001
The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values.
R J, Hathaway, J C, Bezdek
openaire   +2 more sources

Wavelet Frame-Based Fuzzy C-Means Clustering for Segmenting Images on Graphs

IEEE Transactions on Cybernetics, 2020
In recent years, image processing in a Euclidean domain has been well studied. Practical problems in computer vision and geometric modeling involve image data defined in irregular domains, which can be modeled by huge graphs.
Cong Wang, W. Pedrycz, Jianbin Yang, Mengchu Zhou, Zhiwu Li   +4 more
semanticscholar   +1 more source

Complex fuzzy c-means algorithm

Artificial Intelligence Review, 2011
In this paper a new clustering algorithm is presented: A complex-based Fuzzy c-means (CFCM) algorithm. While the Fuzzy c-means uses a real vector as a prototype characterizing a cluster, the CFCM's prototype is generalized to be a complex vector (complex center). CFCM uses a new real distance measure which is derived from a complex one. CFCM's formulas
openaire   +1 more source

Interval-Valued Fuzzy c-Means Algorithm and Interval-Valued Density-Based Fuzzy c-Means Algorithm

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020
Most of the time membership value in the fuzzy set cannot be exactly defined. Interval-valued fuzzy set (IVFS) is a special type of type-2 fuzzy sets which represents the membership value of the fuzzy set as an interval. IVFS assumes that membership interval can better represent the uncertainty in the data.
Ayush K. Varshney, Priyanka Mehra, Pranab K. Muhuri, Q. M. Danish Lohani   +3 more
openaire   +1 more source