DK-Means——分布式聚类算法K-Dmeans的改进
But, K-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for K-means (DK-means) by exploring a set of ...
New classification algorithm K-means clustering combined with SVDDK-means聚类与SVDD结合的新的分类算法*单值分类支持向量数据描述K-means聚类局部疏密度为了提高支持向量数据描述(SVDD)的分类精度,引入局部疏密度提出了改进的SVDD算法.该算法提高了分类精度,但增加了计算复杂度.为此,先用K-means聚类将整个数据集划分为...
执行K均值聚类: kmeans = KMeans(n_clusters=4) kmeans.fit(X) y_kmeans = kmeans.predict(X) 可视化结果: plt.scatter(X[:,0], X[:,1], c=y_kmeans, s=50, cmap='viridis') centers = kmeans.cluster_centers_ plt.scatter(centers[:,0], centers...
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根据K-means聚类分析法,运用Python语言实现漏损预估模型的建立,其中选择主成分分析算法(PCA)对数据进行降维处理,选择支持向量机算法(SVM)进行机器学习.将研究区域漏损数据带入模型后,绘制K/distortion函数,根据肘部法则将数据聚成五类,对五类数据标注分类... 王琪 - 《大连理工大学》 被引量: 0发表: 2020年 基于Sp...
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letkmeans_fl=(tbl:(*), k:int, features:dynamic, cluster_col:string) {letkwargs = bag_pack('k', k,'features', features,'cluster_col', cluster_col);letcode =```if 1: from sklearn.cluster import KMeans k = kargs["k"] features = kargs["features"] cluster_col = kargs["clust...
STM32WB5MM-DK, The Object Lesson The STM32WB5MM-DK The STM32WB5MMG module is a solution for teams that must delegate the most complex challenges behind wireless designs. It houses the antenna, crystals, and everything else necessary to use the embedded RF. Engineers don’t have to spend...