Fast K-Means for very large datasetsClustering very large datasets is a challenging problem for data mining and processing. MapReduce is considered as a powerful programming framework which significantly reduces executing time by dividing a job into several tasks and executes them in a distributed ...
#1.这是一个由java实现的的,多线程Kmeans聚类算法; #2.在聚类的选种阶段实现了Kmeans++算法和NIPS 2016的文章“Fast and Probably Good Seedings for k-Means”中提出了AFK-MC²算法,该算法改进了k-Means算法中初始种子点的生成方式,使其聚类速度相较于目前最好的k-Means++方式提高了好几个数量级。
Raied Salman,Vojislav Kecman,Qi Li.FAST K-MEANS ALGORITHM CLUSTERING..Raied Salman,Vojislav Kecman,Qi Li.FAST K-MEANS ALGORITHM CLUSTERING. .Fast k-means algorithm clustering. Salman R,Kecman V,Li Q, et al. InternationalJournal of Computer Networks&Communications . 2011...
Fast K-means clustering (https://www.mathworks.com/matlabcentral/fileexchange/33541-fast-k-means-clustering), MATLAB Central File Exchange. Retrieved January 29, 2025. Requires MATLAB For a full usage, a OpenMP C compiler compliant such MSCV/Intel compiler/GCC. Shoud work with LCC but ...
This repo holds the source code and scripts for reproducing the key experiments of fast k-means evaluation. We also upload an exemplar dataset that you can play with in the folder "dataset". Download our technical report here:https://github.com/tgbnhy/fast-kmeans/blob/master/unik-tr.pdf ...
k-means++ k-means++: The Advantages of Careful Seeding 这篇文章提出了一种基于采样方法的中心点初始化方法。具体做法的思想是选取使各个中心点尽可能地远离,来减少由于中心点相邻而导致的误差(也不算是误差,错误?大概是这个意思,笔者语文学的还不如英语)。 具体的做法就是随机先选取一个初始中心点,然后计算各...
from fast_pytorch_kmeans import KMeans 轮廓系数 OPENCV:Kmeans的四个轮廓角点,进行逆时针排序。 代码整体思路为: canny提取轮廓 开闭操作 提取最大轮廓(实际应用对象为一个带圆角的矩形) 多边形拟合轮廓 轮廓分割的比较好的话 使用Kmeans 聚类四个点
We consider the k-means problem in the situation where the data is too large to be stored in main memory and must be accessed sequentially, such as from a disk, and where we must use as little memory as possible. Our algorithm is based on recent theoretical results, with significant ...
Due to the generality of the approach, we propose to apply AOG to an efficient stream clustering technique: Very Fast K-Means (VFKM). It is an extension of K-Means for data stream clustering. VFKM is able to deal with continuous data rather than a static dataset. In this paper, we ...
然后在此基础上,读了《Fast and Provably Good Seedings for k-means》。这篇文章是在文章《Approximate k-means++ in sublinear time》的基础上改进的。 从速度上予以提高,在准确性上予以提高。 主要原理是利用MCMC,马尔科夫链蒙特卡罗方法。 具体还没看懂。