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(:,...
function [idx, C, sumD, D] = kmeans(X, k, varargin)%KMEANS K-means clustering.% IDX ...
Examples --- >>> from sklearn.cluster import KMeans >>> import numpy as np >>> X = np.array([[1, 2], [1, 4], [1, 0], ... [4, 2], [4, 4], [4, 0]]) >>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X) >>> kmeans.labels_ array([0, 0, 0, 1,...
| 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...
Objective: Utilize k-means 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...
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 the n-by-p data matrix X into k...
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 the n-by-p data matrix X into k...
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...
However, instead of using linear programming in the assignment phase, we formulate the partitioning as a pairing problem [7], which can be solved optimally by Hungarian algorithm in O(n3) time. Balanced K-Means 35 Table 1. Classification of some balanced clustering algorithms Balance-constrained ...
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