Search - kmean algorithm in c

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[Mathimatics-Numerical algorithms] exer-kmean DL : 0: k均值算法实现聚类 c语言编写-k-means clustering algorithm c language
Date : 2025-07-15 Size : 29kb User : 胡素芳
[.net] Kmeans DL : 0: 在Visual C++.NET2003平台下实现聚类算法中的K-MEANS算法，对随机生成的点进行了聚类。使用单文档结构，并将聚类结果显示出来。-In the Visual C++. NET2003 platform clustering algorithm to achieve the K-MEANS algorithm, on randomly generated points in the cluster. Use a single document structure, and clustering results are displayed.
Date : 2025-07-15 Size : 62kb User : 杨维斌
[AI-NN-PR] kmean DL : 0: 模式识别算法 k均值和感知器算法的具体实现实例-Pattern recognition algorithm for k-means algorithm and the perceptron realize specific examples
Date : 2025-07-15 Size : 173kb User : fengyuan
[AI-NN-PR] kmean DL : 0: kmean算法的c++实现，在vs2003上编译通过。-kmean algorithm c++ realize, in the VS2003 compiled through.
Date : 2025-07-15 Size : 981kb User : ETSmallville
[AI-NN-PR] KMEAN DL : 0: 聚类分析算法中的最基本的K均值算法C++实现程序-Cluster analysis algorithm of the basic K-means algorithm C++ Realize procedures
Date : 2025-07-15 Size : 1kb User : 李超
[Algorithm] kmean DL : 0: K-Means Clustering Algorithm implemented in C
Date : 2025-07-15 Size : 3kb User : Alak Roy
[matlab] kmean DL : 0: 包括K-均值聚类算法的思想介绍，kmeans的MATLAB代码，c语言代码、c++代码。-Including the K-means clustering algorithm introduced the idea, kmeans of MATLAB code, c language code, c++ code.
Date : 2025-07-15 Size : 10kb User : 刘斌
[Algorithm] KMean DL : 0: KMEAN C# In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard), however there are efficient heuristic algorithms that are commonly employed and converge fast to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data, however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.
Date : 2025-07-15 Size : 2kb User : Truong