In this proposed method, the global kernel k-means clustering algorithm is an extension of the standard k-means clustering algorithm and has been used to identify or classify clusters that are non-linearly separated in space input. This method adds one cluster at each stage through a global ...
Research on Density-Based K-means Clustering Algorithm Cluster analysis is an unsupervised learning process, and its most classic algorithm K-means has the advantages of simple principle and easy implementation. In view of the K-means algorithm's shortcoming, where is arbitrary processing of... S...
By applying a clustering algorithm such as K-Means, three clusters could be found (represented by the blue, red and green colors): Intuitively, these clusters somewhat make sense as they are groups of points that are close to each other. Clustering has many applications. It can be used for...
K-Medians is another clustering algorithm related to K-Means, except instead of recomputing the group center points using the mean we use the median vector of the group. This method is less sensitive to outliers (because of using the Median) but is much slower for larger datasets as sorti...
aThe k -means algorithm (MacQueen, 1967; Anderberg, 1973), one of the mostly used clustering algorithms, is classified as a partitional or nonhierarchical clustering method (Jain and Dubes, 1988). Given a set of numeric objects X and an integer number k (≤n),the k -means algorithm...
ten clusters were generated using the QGIS K-Means clustering plugin. In terms of selecting the number of clusters, a balance was needed between geographical coverage of the clusters and number of incidents contained within the clusters. The fewer number of clusters, the larger the geographic area...
This software is dervied from Professor Wei-keng Liao's parallel k-means clustering code obtained on November 21, 2010 fromhttp://users.eecs.northwestern.edu/~wkliao/Kmeans/index.html(http://users.eecs.northwestern.edu/~wkliao/Kmeans/simple_kmeans.tar.gz). With his permission, I am publi...
Speaker recognition using fuzzy C-mean clustering algorithm and vector-quantization(VQ) algorithm模糊C-均值(FCM)聚类法与矢量量化法相结合用于说话人识别 In this paper, an efficient method for speaker recognition-the combination of VQ (Vector-Quantization) algorithm with fuzzy C-mean clustering algorithm...
These PSO heuristics can make the K-means algorithm more stable for finding better solutions and less dependent on the initial cluster centers based on the preliminary experimental results. 展开 关键词: K-Means clustering particle swarm optimization ...
Since scCCESS can be used with any clustering algorithm that allows user-specified k values, we coupled scCCESS with a basic k-means clustering algorithm and SIMLR [25], a single-cell specific clustering algorithm. We refer to them as scCCESS-Kmeans and scCCESS-SIMLR, respectively, ...