The minimum sum-of-squares clusering is the most widely used clustering method. The minimum sum-of-squares clustering is usually solved by the heuristic KMEANS algorithm, which converges to a local optimum. A lot of effort has been made to solve such kind of problems, but a mixed integer ...
将新算法QACS 与K-means聚类算法相结合，提出了一种新的串行K-means聚类算法（K-means clustering algorithm based on QACS，QACS-KMeans），提高了K-means聚类算法的全局搜索能力；③针对K-means聚类算法在处理较大数据量时效率较低的问题，利用Hadoop分布式平台的MapReduce编程模型实现了对新算法QACS-KMeans 的...
The computational results are competitive when compared with the optimal solutionprovided for the problemsS. GeethaG. PoonthalirP. T. VanathiGeetha S, Poonthalir G, Vanathi P (2009) Improved K-means algo- rithm for capacitated clustering problem. Internat. INFOCOMP J. Comput. Sci. 8(4):...
K-Means for Clustering is one of the popular algorithms for this approach. Where K means the number of clustering and means implies the statistics mean a problem. It is used to calculate code-vectors (the centroids of different clusters). According to a tutorial, for any word/value/key that...
A Novel Clustering Algorithm Based on Hierarchical and K-means Clustering Although the priority and randomicity to initiate clustering centers of K-means have been solved by traditional hierarchical k-means clustering algorithm, ... W Li,Z Yong,S Xia - IEEE 被引量: 33发表: 2007年 A Hybrid ...
The developed software package for solving clustering problems by the method of K - means with using GPUs allows us to handle up to 2 million records with number of features up to 25. The gain in the computing time is in factor 5. We plan to increase this factor up to 20-30 after ...
A Novel Clustering Algorithm Based on Hierarchical and K-means Clustering Although the priority and randomicity to initiate clustering centers of K-means have been solved by traditional hierarchical k-means clustering algorithm, ... W Li,Y Zhou,S Xia 被引量: 23发表: 2007年 Hierarchical and K-...
Kernel fuzzy c-means (KFCM) [34] is a significantly improved version of fuzzy c-means for clustering linearly inseparable datasets. However, the problem of KFCM with fuzzification parameter 𝑚=1m=1 cannot be solved by existing methods. To solve the special case of KFCM for 𝑚=1m=1, a ...
The ultimate guide to K-means clustering algorithm - definition, concepts, methods, applications, and challenges, along with Python code.
public static void ComputeVector (Clustering.Library.Image image) { // Convert image data into the k-means algorithm-specific format: // float[pixelCount][3] float [, ] data = null; data = new float [image.SizeInPixels, Clustering.Library.DominantColor.VectorLength]; for (int i=0; i<...