Examples collapse all Train a k-Means Clustering Algorithm Copy Code Copy Command Cluster data using k-means clustering, then plot the cluster regions. Load Fisher's iris data set. Use the petal lengths and widths as predictors. Get load fisheriris X = meas(:,3:4); figure; plot(X(:,...
| init : {'k-means++', 'random', ndarray, callable}, default='k-means++' | Method for initialization:#设置初始中心点如何产生 | | 'k-means++' : selects initial cluster centers for k-mean | clustering in a smart way to speed up convergence. See section | Notes in k_init for more...
K-means clustering Syntax IDX = kmeans(X,k) [IDX,C] = kmeans(X,k) [IDX,C,sumd] = kmeans(X,k) [IDX,C,sumd,D] = kmeans(X,k) [...] = kmeans(...,param1,val1,param2,val2,...) Description IDX = kmeans(X,k)partitions the points in then-by-pdata matrixXintokcluste...
K-means clustering Syntax IDX = kmeans(X,k) [IDX,C] = kmeans(X,k) [IDX,C,sumd] = kmeans(X,k) [IDX,C,sumd,D] = kmeans(X,k) [...] = kmeans(...,param1,val1,param2,val2,...) Description IDX = kmeans(X,k)partitions the points in then-by-pdata matrixXintokcluste...
function [idx, C, sumD, D] = kmeans(X, k, varargin)%KMEANS K-means clustering.% IDX ...
def k_means(X, n_clusters, init='k-means++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=1e-4, random_state=None, copy_x=True, n_jobs=1, algorithm="auto", return_n_iter=False): """K-means clustering algorithm. Read more in the :ref:`User Guid...
Objective: Utilize kmeans clustering to segment customers of a mall based on their spending behavior, aiming to provide personalized services and improve marketing strategies. Dataset: Use the "Mall Customer Segmentation Data" available on the UCI Machine ...
random_state=random_state) centers = check_array( centers, dtype=X.dtype, copy=False, order='C') self._validate_center_shape(X, centers) if sp.issparse(centers): centers = centers.toarray() return centers def fit(self, X, y=None, sample_weight=None): """Compute k-means clustering....
examples plotter .gitignore .golangci-soft.yml .golangci.yml LICENSE README.md go.mod go.sum kmeans.gif kmeans.go kmeans_test.go README MIT license kmeans k-means clustering algorithm implementation written in Go What It Does k-means clusteringpartitions a multi-dimensional data set intok...
3kmeans函数 function [idx, C, sumD, D] = kmeans(X, k, varargin) %KMEANS K-means clustering. % IDX = KMEANS(X, K) partitions the points in the N-by-P data matrix % X into K clusters. This partition minimizes the sum, over all % clusters, of the within-cluster sums of poin...