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Fuzzy C-Means Clustering Algorithm

A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C Means Clustering Algorithm

A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C Means Clustering Algorithm

... Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better ...

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Fuzzy C -Means Clustering Algorithm for Optimization of Routing Protocol in Wireless Sensor Networks

Fuzzy C -Means Clustering Algorithm for Optimization of Routing Protocol in Wireless Sensor Networks

... on clustering is not only scalable with the dimension of networks but also offers efficient management of ...the fuzzy c-means clustering ...LEACH- C and CHEF protocols with ...

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Using fuzzy c-means clustering algorithm for common lecturer timetabling among departments

Using fuzzy c-means clustering algorithm for common lecturer timetabling among departments

... two clustering and traversing agents are used where the former is to cluster common lecturers among departments and the latter is to find unused resources among ...the clustering and traversing processes, ...

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Plant Leaf Disease Detection Using Fuzzy C-Means Clustering Algorithm

Plant Leaf Disease Detection Using Fuzzy C-Means Clustering Algorithm

... training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a ...

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A cooperative spectrum sensing method based on information geometry and fuzzy c means clustering algorithm

A cooperative spectrum sensing method based on information geometry and fuzzy c means clustering algorithm

... and fuzzy c-means clustering algorithm is proposed in this ...the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding ...

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Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation

Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation

... From the experience of a number of researchers, the manual segmentation approach is difficult to perform and requires a comparatively longer time period. The desired approach is automated brain tumor segmentation. The ...

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A Review on Image Segmentation by Fuzzy C-Means Clustering Algorithm

A Review on Image Segmentation by Fuzzy C-Means Clustering Algorithm

... Then, they introduce the use of kernel-induced distances instead of the usual Euclidean one. The corresponding algorithms are respectively denoted as KFCM_S1 and KFCM_S2 in the sequel. Moreover, since they use kernel- ...

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An Improved Fuzzy C Means Clustering Algorithm Based on Potential Field

An Improved Fuzzy C Means Clustering Algorithm Based on Potential Field

... FCM algorithm that the initial clustering centers is overly ...evolutionary algorithm which motivated by migration mechanism of ...K-means clustering instead of the random selection ...

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Scalable Parallel Clustering Approach for Large Data using Possibilistic Fuzzy C Means Algorithm

Scalable Parallel Clustering Approach for Large Data using Possibilistic Fuzzy C Means Algorithm

... k-means clustering algorithm based on the message passing ...improved fuzzy clustering-text clustering method based on the fuzzy C-Means clustering ...

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Efficient Early Risk Factor Analysis of Kidney Disorder Using Data mining Technique

Efficient Early Risk Factor Analysis of Kidney Disorder Using Data mining Technique

... designed algorithm for clinical decision support system shown in figure 2 contains “ ” input vector of the form = { 1, 2, …, 13}, and two output levels of the form = { 1, 2}, where the two values are predicted as ...

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Online Full Text

Online Full Text

... This paper presents a novel multiple nucleus detec- tion schemes which include the protozoan parasite era- sure, gamma equalization, and fuzzy C-means clustering algorithm; modified ...

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Title: APPLICATION OF COLOR BASED IMAGE SEGMENTATION PARADIGM ON RGB COLOR PIXELS USING FUZZY C-MEANS AND K MEANS ALGORITHMS

Title: APPLICATION OF COLOR BASED IMAGE SEGMENTATION PARADIGM ON RGB COLOR PIXELS USING FUZZY C-MEANS AND K MEANS ALGORITHMS

... K-means clustering algorithm in different color spaces of color image ...K-means clustering algorithm divides into K clusters based on the similarity between the pixels in that ...

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A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity

A New Clustering Validity Index for Fuzzy C Means Algorithm Based on Measure Of Disparity

... for fuzzy clustering in order to eliminate the monotonically decreasing tendency as the number of clusters approaches to the number of data points and avoid the numerical instability of validation index ...

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Context-Based Gustafson-Kessel Clustering with Information Granules

Context-Based Gustafson-Kessel Clustering with Information Granules

... (CGK) clustering that builds Information Granulation (IG) in the form of fuzzy ...this clustering is based on Conditional Fuzzy C-Means (CFCM) clustering introduced by ...

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Application of Modified K Means Clustering Algorithm in Segmentation of Medical Images of Brain Tumor

Application of Modified K Means Clustering Algorithm in Segmentation of Medical Images of Brain Tumor

... this algorithm allows the pixel to get placed in multiple classes with varying degrees of membership and it is based on the minimization of the following objective ...FCM algorithm attempts to partition a ...

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Bilateral Weighted Fuzzy C-Means Clustering

Bilateral Weighted Fuzzy C-Means Clustering

... the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion ...this clustering algorithm may be significantly degraded in ...

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Segmentation of MR images for Tumor extraction by using clustering algorithms

Segmentation of MR images for Tumor extraction by using clustering algorithms

... K-means, Fuzzy c-means (FCM) clustering algorithm has been used in medical image segmentations, but the disadvantage of the k-means algorithm is weak pixel ...

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Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

... For the two remaining datasets, features were already normalized aside from the pre-processing technique. Observation of the actual content of the dataset was also needed to be observed thoroughly to see how these ...

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IMPROVED FUZZY C MEANS ALGORITHM BASED ON ROBUST INFORMATION CLUSTERING FOR IMAGE SEGMENTATION

IMPROVED FUZZY C MEANS ALGORITHM BASED ON ROBUST INFORMATION CLUSTERING FOR IMAGE SEGMENTATION

... the algorithm has converged (that is, the coefficients' change between two iterations is no more than, the given sensitivity threshold) Compute the centroid for each cluster, using the formula ...The ...

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A Novel Uncertain Fuzzy C-Means Clustering Technique Using Genetic Algorithm (UFCM-GA)

A Novel Uncertain Fuzzy C-Means Clustering Technique Using Genetic Algorithm (UFCM-GA)

... Toon Calders et. al. proposed classical frequent itemset mining algorithms for deterministic data sets, and compared their relative performance in terms of efficiency and memory usage. The UH-mine algorithm, ...

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