The basic K-means is sensitive to the initial centre and easy to get stuck at local optimal value. To solve such problems, a new clustering algorithm is proposed based on simulated annealing. The algorithm views the clustering as optimization problem, the bisecting K-means splits the dataset ...
聚类算法(一)--Kmeans 原始Kmeans原理: Kmeans为无监督学习(即样本无标签,简单理解为没有Y值,只有X) Kmeans将给定的样本分为k个类,每一类成为一簇(clustering),目标是让每一簇样本紧密联系,簇与簇之间间隔较大 数学公式表示: 假设样本分(C1,C2,,Ck)(C1,C2,,Ck),则优化目标为最小化平方误差E: E=∥x...
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 Clustering TechniqueThis paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in...
Aiming at the fault line selection problem in the single-phase grounding system of the distribution network, a new fault line selection method based on VMD and permutation entropy feature extraction combined with K-means clustering algorithm is proposed. This method is a hybrid algorithm that can ...
Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering, called GK-Means. The ...
Each replicate samples s rows from X (specified by 'NumSamples' name-value pair argument) to perform clustering on. By default, 40+2*k samples are selected. 'large' This is similar to the small scale algorithm and repeatedly performs searches using a k-means like update. However, the ...
To investigate the hot topics and attitudes of autism in the larger community. In this study, we analyzed and summarized experimental texts from the social media platform Zhihu using the TF-IDF algorithm and K-means clustering approach. Based on the anal
After considering the prevalent limitation in other techniques, the present study used random selection and k-means clustering techniques to select micro-trips from the entire data pool to arrive the best representative driving cycle for e-rickshaw. The sequence of driving data between two successive...
l (3) This is a weighted K-means clustering problem. One usually solves it using some form of iterative scheme that converges to a local minimum. A classic way of solving it, is alternating between estimating the cluster centers cl and the assignment of points ui to clusters. If we have...