First, an improved K-means clustering algorithm for global earthquake catalogs is proposed. Traditional K-means clustering has several limitations, i.e., the number of clusters needs to be initialized, the initial cluster centers are arbitrarily selected, and there is currently no magnitude parameter...
In our paper for the purpose of initializing the initial centroids of the Improved Hybridized K Means clustering algorithm (IHKMCA) we make use of genetic algorithm, so as to get a more accurate result. The results thus found from the proposed work have better accuracy, more efficient and ...
Clustering with Bregman divergences J. Mach. Learn. Res. (2005) V. Tunali et al. An improved clustering algorithm for text mining: multi-cluster spherical k-means Int. Arab J. Inf. Technol. (2016) S.V. Ault et al. On speech recognition algorithms Int. J. Mach. Learn. Comput. (2018...
In this, we have provided an improved clustering algorithm for segmenting customers using RFM values and compared the performance against the traditional techniques like K-means, single link and complete link.Prabha DhandayudamIlango Krishnamurthi...
Keywords:clustering;K-meansalgorithm;PSOalgorithm;globaloptimum 0摇引摇言 聚类分析是一种无监督分类技术,按照一定的相 似性标准将数据集进行分类,使得类内的对象尽可能 相似,而不同类之间的对象尽可能相异 [1-2] 。K- means算法 [3] 是基于划分的经典聚类算法,具有容易 理解、实现简单、收敛速度快等许多优...
K-means algorithm is one of the most popular clustering algorithms. However, it is sensitive to initialized partition and the circular dataset. To attack this problem, this paper introduced an improved k-means algorithm based on multiple feature points. The algorithm selects a number of feature ...
An Improved K-means Algorithm and Its Application in the Evaluation of Air Quality LevelsThis research introduces an improved k-means algonthm which combines hierarchical method with k-means method and the application in the evaluation of air quality levels.In present,evaluation of air quality ...
3.1. K-means clustering ALGORITHM The approach of k-means is based on spherical clusters in which the data points converge surrounding the cluster’s centroid. The k-means splits a set of data points X=x1,x2,x3,⋯,xN into k known number of clusters. Randomly, the k-means selects k...
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlappi...
Finally, K-Means algorithm will be integrated with Compute Unified Device Architecture (CUDA). The time efficiency is improved considerably through taking advantage of computing power of Graphic Processing Unit (GPU). We use the ratio of distance between classes to distance within classes and speedup...