The K-means algorithm is a basic and most widely used division method among clustering analysis methods. It is a method of discovering clusters and cluster centers in unclassified labeled data [9]. Its main advantage is that the algorithm is simple and fast. If the resulting clusters are dense...
A sampling-pso-k-means algorithm for document clustering. KAMEL N,OUCHEN I,BAALI K. . 2014Nadjet Kamel, Imane Ouchen, Karim Baali, "A Sampling PSOKmeans Algorithm for Document Clustering", Advances in Intelligent Systems and Computing, Springer, 45-54, 2013....
K-means clusteringis a robust unsupervised clustering method. K-means algorithm uses the criteria of squared error, likeEuclidean distancemeasure, for calculating the distance between the data points for the process of grouping. K-means follows the typical process of clustering with first initializing ...
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...
K-means Algorithm, and Mean-Variance Model named as TAKMV method. The diversification of this study will help to protect an investor's portfolio from the systematic risk that could expose the portfolio to losses. The TAKMV method was introduced to help investors, traders, managers, and decision...
Overview of the proposed hybrid oil price prediction model. This flowchart illustrates the three-phase training process: initial training of the DSD-LSTM model, application of the K-means algorithm to form clusters within the data, and subsequent fine-tuning of duplicated DSD-LSTM models for each...
K-中心点算法也是一种常用的聚类算法,K-中心点聚类的基本思想和K-Means的思想相同,实质上是对K-means算法的优化和改进。在K-means中,异常数据对其的算法过程会有较大的影响。在K-means算法执行过程中,可以通过随机的方式选择初始质心,也只有初始时通过随机方式产生的质心才是实际需要聚簇集合的中心点,而后面通过不...
K-中心点算法也是一种常用的聚类算法,K-中心点聚类的基本思想和K-Means的思想相同,实质上是对K-means算法的优化和改进。在K-means中,异常数据对其的算法过程会有较大的影响。在K-means算法执行过程中,可以通过随机的方式选择初始质心,也只有初始时通过随机方式产生的质心才是实际需要聚簇集合的中心点,而后面通过不...
Initially, data items are grouped using the k-means algorithm in an offline clustering phase. The second phase involves an online search where the TI approach identifies potential k nearest neighbors for a given query. This method accelerates the kNN search as distances between all items and the...
8) 将Hausdorff距离和k-means算法提取的结果进行融合,得到最终的精简数据。融合k-means聚类和Hausdorff距离的点云精简算法流程如图 3所示。图? 3??融合k-means聚类和Hausdorff距离的点云精简算法流程 Figure? 3.??Flowchart of Point Cloud Simplification Algorithm Integrating?k-means Clustering and Hausdorff ...