[PDF] Top 20 A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets
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A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets
... Abstract- Data clustering is an unsupervised method for extraction hidden pattern from huge data ...for high dimensional data sets with enormous number of samples is a ... See full document
5
Image Classification through integrated K- Means Algorithm
... years. Clustering analysis is a valuable and useful tool for image classification and object ...of clustering algorithms are available and still this is a topic of interest in the image processing ...these ... See full document
7
A NOVEL KERNEL BASED FUZZY C MEANS CLUSTERING WITH CLUSTER VALIDITY MEASURES
... fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed by Yang et al (2011) to deal with the classification problems ... See full document
9
AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
... partitional clustering algorithm in the remote sensing ...this algorithm is highly sensitive to the initial placement of cluster ...the high-dimensional data such as ... See full document
5
Infected Fruit Part Detection using K-Means Clustering Segmentation Technique
... a novel defect segmentation of fruits based on color features with K-means clustering unsupervised ...clustered based on their color and spatial features, where the ... See full document
8
Text Extraction from Live Captured Image with Diversified Background using Edge Based & K-Means Clustering
... Abstract - The proposed system highlights a novel approach of extracting a text from image using K-Means Clustering. Text Extraction from image is concerned with extracting the relevant text ... See full document
7
Classification Of Cluster Area Forsatellite Image
... networks, clustering method, fuzzy-sets, and expert systems have been widely applied for the problem of image ...the K-means clustering algorithm that is unsupervised learning ... See full document
5
Color Image Segmentation via Improved K-Means Algorithm
... —Data clustering techniques are often used to segment the real world ...are based on the clustering suffer from random ...segmentation algorithm, which can be used in the computer ... See full document
8
Mineral Detection using K-Means Clustering Technique
... a novel algorithm formulated with k-means clustering performed on remote sensing ...the data acquisition method and data processing ...The k-means ... See full document
8
Dynamic Clustering Of High Speed Data Streams
... popular clustering algorithms used. K-Means Technique uses a Partitioning ...Partitioning algorithm constructs various partitions for the data elements and then evaluates them by some ... See full document
5
Automatic Clustering Approaches Based On Initial Seed Points
... Abstract--Since clustering is applied in many fields, a number of clustering techniques and algorithms have been proposed and are available in the ...a novel approach to address the major problems in ... See full document
7
Investigation of Internal Validity Measures for K-Means Clustering
... the k-means algorithm is to choose the number of clusters into which the data will be ...of k is largely an interpretive decision. Suc- cessive runs of k-means can ... See full document
6
Parallel K-Means Algorithm on Agricultural Databases
... in data mining algorithms as the following: (1) Independent parallelism where each processor accesses to the whole data to operate but do not communicate each ...whole data set. (3) SPMD (Single ... See full document
4
A Novel Density based improved k-means Clustering Algorithm – Dbkmeans
... in data mining and is an active research topic for the ...of clustering is to partition a set of objects into clusters such that objects within a group are more similar to one another than patterns in ... See full document
6
A Cluster Feature-Based Incremental Clustering Approach to Mixed Data
... feature- based approach to incremental clustering of mixed ...initial clustering and incremental clustering. K-means algorithm is used for initial clustering, in ... See full document
6
A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.
... a novel method to predict GIs that is built upon mean shift clustering ...calculated based on a heuristic ...different novel unpredicted ... See full document
12
Automatic human activity segmentation and labeling in RGBD videos
... sensor based on the information about the position of the main skeleton ...labeled data to perform supervised ...a clustering algorithm on a subset of the available ... See full document
12
Visualizing Structures in Confocal Microscopy Datasets Through Clusterization: A Case Study on Bile Ducts
... microscopy data and require extensive user ...pre-processing, clustering, and 3D visualization. For clustering, we explore the density-based spatial clustering for applications with ... See full document
6
A Robust Background Removal Algortihms Using Fuzzy C-Means Clustering
... provides high efficient for background subtraction which can be applied for vision based applications such as human motion analysis or surveillance ... See full document
9
Joao Santos Oliveira
... algoritmos k-means, fuzzy c-means e subtractive clustering e a uma técnica de aprendizagem supervisionada baseada em máquinas de vectores de ... See full document
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