基于K-均值聚类和泰森多边形的异常值检测方法

离群点发现是数据挖掘研究的一个重要方面.根据数据流的特点提出一种基于K-均值聚类和泰森多边形的离群点检测方法，先用K-均值对数据进行处理，生成中间聚类结果，然后用泰森多边形方法（VOD）对这些中间结果进行再次选择，最后找出可能存在的离群点.     

基于 ICA K-Means 的产品口碑演化聚类与营销分析

对于产品而言,其在线口碑的活跃度是非常具有代表性的一个指标。在线口碑活跃度的高低,直接揭示产品的生命周期演化模式,对于产品生命周期有全面的了解有助于决策者制定营销计划以及战略。但由于产品在线评论的高维性和复杂性,使得其聚类的难度加大。所以,在普通的K均值算法的基础上引入独立成份分析,对异类产品之间或同类产品在线口碑的活跃度之间进行聚类分析,可以大大降低复杂性和提升聚类准确性;同时深入分析提取出的产品生命周期曲线,有效提升在线口碑信息在电子商务营销管理与决策支持中的作用,深化在线口碑活跃度的管理学视角研究。     

K-medoids inverse regression

Abstract

K-means inverse regression was developed as an easy-to-use dimension reduction procedure for multivariate regression. This approach is similar to the original sliced inverse regression method, with the exception that the slices are explicitly produced by a K-means clustering of the response vectors. In this article, we propose K-medoids clustering as an alternative clustering approach for slicing and compare its performance to K-means in a simulation study. Although the two methods often produce comparable results, K-medoids tends to yield better performance in the presence of outliers. In addition to isolation of outliers, K-medoids clustering also has the advantage of accommodating a broader range of dissimilarity measures, which could prove useful in other graphical regression applications where slicing is required.     

Weighting variables in K-means clustering

The aim of this study is to assign weights w ₁, …, w _m to m clustering variables Z ₁, …, Z _m , so that k groups were uncovered to reveal more meaningful within-group coherence. We propose a new criterion to be minimized, which is the sum of the weighted within-cluster sums of squares and the penalty for the heterogeneity in variable weights w ₁, …, w _m . We will present the computing algorithm for such k-means clustering, a working procedure to determine a suitable value of penalty constant and numerical examples, among which one is simulated and the other two are real.     

移动商务中面向客户细分的KSP混合聚类算法

数据挖掘技术中的聚类算法是解决客户细分问题的重要算法之一。为解决传统聚类算法在客户细分问题中分类精度较低、收敛速度较慢的问题,着重对比分析传统聚类算法中K-m eans、自组织映射网络和粒子群3种算法的不足,提出融合3种算法优点的混合型聚类算法,该算法利用K-m eans和自组织映射网络对初始聚类中心进行优化,结合粒子群优化和K-m eans优化聚类迭代过程,并在迭代优化过程中设计避免算法因早熟而停滞的机制。针对移动电子商务环境下的餐饮业客户细分问题,建立移动餐饮业客户细分模型,并利用混合型聚类算法、K-m eans、层级自组织映射网络和基于粒子群的K-m eans等4种算法对实际案例进行对比分析。研究结果表明,混合型聚类算法的聚类精度分别比其他3种算法高,同时还具有最快的收敛性能,更适用于客户细分问题。     

基于K-means聚类算法的远程学习者效果分析

网络技术的迅速发展为远程教育中个性化学习提供了可能。首先使用K—means算法对学生的属性数据和相应课程的成绩进行了聚类数据挖掘,发现学习者群体的特点。然后,结合聚类结果的特性和差异,为课程资源建设及教学过程的改进提供帮助。     

K-均值算法中聚类个数优化问题研究

在传统的K-均值聚类算法中,聚类数K必须事先给定,然而,实际中K值很难被精确的确定,K值是否合理直接影响着K-均值算法的好坏。针对这个缺点,提出一种优化聚类数算法,根据聚类算法中类内相似度最大差异度最小和类间差异度最大相似度最小的基本原则,构建了距离评价函数F（S,K）作为最佳聚类数的检验函数,建立了相应的数学模型,并通过仿真实验进一步验证了新算法的有效性。     

一种基于信息熵与K均值迭代模型的模糊聚类算法

本文提出了基于信息熵和K均值算法混合迭代模糊聚类的细分模型,解决了模糊聚类的原型初始化参数问题。将信息熵和K均值算法引入模糊聚类中进行分析,并结合测试样本数据进行实际分析,与传统方法相比,取得了较好的效果。     

A clustering-based coordinate exchange algorithm for generating G-optimal experimental designs

In the optimal experimental design literature, the G-optimality is defined as minimizing the maximum prediction variance over the entire experimental design space. Although the G-optimality is a highly desirable property in many applications, there are few computer algorithms developed for constructing G-optimal designs. Some existing methods employ an exhaustive search over all candidate designs, which is time-consuming and inefficient. In this paper, a new algorithm for constructing G-optimal experimental designs is developed for both linear and generalized linear models. The new algorithm is made based on the clustering of candidate or evaluation points over the design space and it is a combination of point exchange algorithm and coordinate exchange algorithm. In addition, a robust design algorithm is proposed for generalized linear models with modification of an existing method. The proposed algorithm are compared with the methods proposed by Rodriguez et al. [Generating and assessing exact G-optimal designs. J. Qual. Technol. 2010;42(1):3–20] and Borkowski [Using a genetic algorithm to generate small exact response surface designs. J. Prob. Stat. Sci. 2003;1(1):65–88] for linear models and with the simulated annealing method and the genetic algorithm for generalized linear models through several examples in terms of the G-efficiency and computation time. The result shows that the proposed algorithm can obtain a design with higher G-efficiency in a much shorter time. Moreover, the computation time of the proposed algorithm only increases polynomially when the size of model increases.