K-means Method
K means method for clustering water quality status on the rivers of Banjarmasin, Indonesia
... The surface river water quality in Banjarmasin city tends to decline constantly as the result of direct and indirect waste disposal from various human activities along the river body. This study aimed to determine the ...
6
Image Segmentation of Cows using Thresholding and K-Means Method
... and K-Means method to produce cow body extraction as an early stage in the process of estimating cow’s ...thresholding method begins by inputting a digital image then performing a sharpened ...
6
Application of k-means method to pattern recognition in on-line cable partial discharge monitoring
... novel method for autonomous recognition of PD patterns recorded under conditions in which a phase-reference voltage waveform from the HV conductors is not available, as is often the case in on-line PD based ...
9
Classification of EU Countries in the Context of Corporate Income Tax
... Non-hierarchical method of uncertain clustering (fuzzy cluster analysis) The fuzzy c‑means method allows for the object ‑ the country ‑ to belong to all clusters ...This method was used to ...
10
A network science-based k-means++ clustering method for power systems network equivalence
... the k-means algorithm for clustering, followed by “AED-based k-means bus clustering method” section presenting our proposed AED- based k-means clustering method, ...
25
Comparison of Digital Image Segmentation Techniques- A Research Review
... fuzzy method. K means method can be done through the particular value of k and the fuzzy techniques by using the different level segmentation of the images ...the K Means ...
6
Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback
... constrained k-means method, the user ...constraint k-means method based on user feedback is ...constrained k-means algorithm in order to obtain the most appropriate ...
9
Overlapping Patterns Recognition with Linear and Non Linear Separations using Positive Definite Kernels
... overlapping k-means method to detect overlapping patterns in unlabeled data ...proposed method performs all the learning process in a high di- mensional feature space where data are explicitly ...
8
An Enhanced Method for Randomized Dimensionality Reduction Using Roughset Based K-Means Clustering
... computing k-median and k-means clustering for points in low ...the k-median/means clustering on S instead of on P, and get an ...a k-means clustering ...the ...
7
Performance Analysis of Improved K-Means & K-Means in Cluster Generation
... quality k-means method gets simply unfree during a native minimum and completely different initial centroids result in different results, if we discover sure initial centroids that are according to ...
7
GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING
... This method was one of the first techniques used for segmenting natural images due to its simplicity and efficiency ...clustering method is depends on its ability to discover most of the hidden ...
9
A novel intrusion detection method based on OCSVM and K-means recursive clustering
... proposed K−OCSVM method ...the method raises since less profound attacks are ...the method degrades to the initial OCSVM. The optimum value for parameter K−OCSVM varies according to the ...
10
Evaluation Of Fuzzy K-Means And K-Means Clustering Algorithms In Intrusion Detection Systems
... this method samples are divided into categories with similar members ...is K-Means algorithm which starts with a set of K reference points and data points belong to K cluster based on ...
7
EBK Means: A Clustering Technique based on Elbow Method and K Means in WSN
... and K-means based quick clustering algorithms to produce a new cluster scheme for WSNs with dynamic selection of the number of the clusters ...proposed method, finding an optimum „k‟ value is ...
8
Detection Of Affected Regions Of Disease Arecanut Using K-Means And Otsu Method
... A image has to be divided to sub images or parts to capture vital information for further work. This process is known as segmentation and is one of the challenging task of image processing. This is carried considering ...
5
CLASSIFICATION BY K MEANS CLUSTERING
... (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each ...re-calculate k new centroids as barycenters of the clusters resulting from the previous ...these k ...
5
EFFICIENT K MEANS CLUSTERING ALGORITHM USING RANKING METHOD IN DATA MINING
... Improved k-means Clustering” discuss an iterative approach which is beneficial in reducing the number of iterations from k- mean algorithm , so as to improve the execution time or by reducing the ...
7
Parallel Implementation of Improved K-Means Based on a Cloud Platform
... effectiveness, K-Means clustering algorithm is widely used to solve various problems in real applications, such as text evolutionary analysis, image clustering, community detection, ...[9]. ...
9
Clustering of Students Based on Principal Factor Iteration
... At present, the clustering of students is mainly based on various clustering methods[1-4]. The clustering algorithms can be divided into two classes: hierarchical clustering algorithms and partitional clustering ...
6
STRATIFIED SAMPLING VOXEL CLASSIFICATION FOR SEGMENTATION OF OCT IMAGES USING NORMALIZED GRAPH CUT SEGMENTATION WITH MFCM CLUSTERING
... We reported a fluid-associated abnormality detection and segmentation method in this manuscript. Detection and segmentation of fluid-associated abnormalities in clinical data is a challenging problem in several ...
7