Search - K-medoids

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[AI-NN-PR] K－均值聚类算法C++编程 DL : 0: K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.
Date : 2025-12-24 Size : 4kb User : 韩磊
[AI-NN-PR] k-means DL : 0: 这是K均值算法,采用c语言编写,K的取值为2,大家可以改变K的值来进行测试-This is the K-means algorithm, using c language, K value of 2, we can change the value of K for testing
Date : 2025-12-24 Size : 313kb User : Gang Li
[AI-NN-PR] dataMining DL : 0: 数据挖掘的软件，集成了关联规则、k-均值聚类、模糊聚类、k-中心点聚类四种算法-software of data mining
Date : 2025-12-24 Size : 145kb User : lqinggui
[AI-NN-PR] k-medoids123 DL : 0: k-medoids算法，用c++实现，一种经典的聚类算法-k-medoids algorithm, c++ to achieve a classic clustering algorithm
Date : 2025-12-24 Size : 3kb User : tom
[AI-NN-PR] Cpp1 DL : 0: 距离与相异度，然后介绍一种常见的聚类算法——k均值和k中心点聚类-Distance and dissimilarity, and then introduce a clustering algorithm- k mean and k-medoids clustering
Date : 2025-12-24 Size : 2kb User : 朱青
[AI-NN-PR] k_medoids DL : 0: 数据挖掘 k中心点算法 matlab示例-data mining k-medoids matlab example
Date : 2025-12-24 Size : 1kb User : 李杰
[AI-NN-PR] K_medoids DL : 0: 聚类算法k-medoids 该代码针对的是图像处理该算法采用局部航迹与系统航迹进行关联的策略-K-medoids clustering algorithm is the code for the image processing algorithm strategy using a local track trajectories associated with the system
Date : 2025-12-24 Size : 565kb User : 孙伟
[AI-NN-PR] K-mean DL : 0: 聚类算法中的k-means算法，和k-medoids 肯定是非常相似的。k-medoids 和 k-means 不一样的地方在于中心点的选取，在 k-means 中，我们将中心点取为当前 cluster 中所有数据点的平均值。-Clustering algorithm k-means algorithm, and k-medoids certainly very similar. k-medoids and k-means not the same place that the center of the selection, the k-means, we will take the average of the center point of all the data points in the current cluster.
Date : 2025-12-24 Size : 22kb User : 赵小娟
[AI-NN-PR] Desktop DL : 0: 传统的K-medoids聚类算法的聚类结果随初始中心点的不同而波动，且计算复杂度较高不适宜处理大规模数据集；快速K-medoids聚类算法通过选择合适的初始聚类中心改进了传统K-medoids聚类算法，但是快速K-medoids聚类算法的初始聚类中心有可能位于同一类簇。为了克服传统的K- medoids聚类算法和快速K-medoids聚类算法的缺陷，提出一种基于粒计算的K-medoids聚类算法。-Traditional clustering K-medoids clustering algorithm with the initial centers Different swings, and a high degree of computational complexity inappropriate handling large data sets Fast K-medoids clustering algorithm by selecting appropriate initial cluster centers to improve The traditional K-medoids clustering algorithm, but fast clustering algorithm K-medoids The initial cluster centers may be located in the same class clusters. In order to overcome the traditional K- Medoids defect clustering algorithm and fast K-medoids clustering algorithm, K-medoids one kind of clustering algorithm based on granular computing.
Date : 2025-12-24 Size : 2kb User : 问建丽