点簇聚合的目标顶点的优化

研究了点簇聚合的目标顶点位置的计算问题。当计算过程中得到的目标顶点不在小单元之内,或者虽然在小单元之内,但目标顶点的位置不能唯一确定时,则将求解目标顶点的问题转化为求解带约束的二次优化问题。此二次优化问题的解既能保证目标顶点位于小单元之内,在位置上又最接近该点簇的重心。实验结果表明,该算法的时间效率类似于Lindstrom的算法,但在简化质量上要优于后者。     

A sequential fitting procedure for linear data analysis models

A particular factor analysis model with parameter constraints is generalized to include classification problems definable within a framework of fitting linear models. The sequential fitting (SEFIT) approach of principal component analysis is extended to include several nonstandard data analysis and classification tasks. SEFIT methods attempt to explain the variability in the initial data (commonly defined by a sum of squares) through an additive decomposition attributable to the various terms in the model. New methods are developed for both traditional and fuzzy clustering that have useful theoretic and computational properties (principal cluster analysis, additive clustering, and so on). Connections to several known classification strategies are also stated.The author is grateful to P. Arabie and L. J. Hubert for editorial assistance and reviewing going well beyond traditional levels.     

中国证券投资基金风格分类研究

差异化战略以及特定资产类别的超额收益促使投资基金在募集时就约定投资风格，但投资基金有可能在实际的运作过程中违背事先约定的投资风格．据此，在投资风格分类中引入了聚类分析方法，并同时采用晨星风格箱方法和聚类分析方法对中国证券投资基金的风格进行了分析．结果表明，大多数基金违背了募集说明书中约定的风格．     

基于自适应性神经网络和历史聚类的旅游规划

研究了不同类型的服务之间的关系,提出了一种基于使用历史的分层聚类,以提取有意义的使用模式,建立了智能旅游规划的自适应型神经网络结构.     

数据挖掘技术在团体医疗保险中的应用

将数据挖掘中的决策树技术应用于团体医疗保险理赔数据库中，提出了基于Clementine数据挖掘平台的理赔风险解决方案，为保险公司团体医疗保险理赔风险规则的提取和费率厘定提供决策依据，以提高团体医疗保险管理的科学性．     

A permutation-based algorithm for block clustering

Hartigan (1972) discusses the direct clustering of a matrix of data into homogeneous blocks. He introduces a stepwise divisive method for block clustering within a certain class of block structures which induce clustering trees for both row and column margins. While this class of structures is appealing, the stopping criterion for his method, which is based on asymptotic theory and the assumption that the individual elements of the data matrix are normally distributed, is quite restrictive. In this paper we propose a permutation-based algorithm for block clustering within the same class of block structures. By using permutation arguments to decide where to split and when to stop, our algorithm becomes applicable in a wide variety of cases, including matrices of categorical data and matrices of small-to-moderate size. In addition, our algorithm offers considerable flexibility in how block homogeneity is defined. The algorithm is studied in a series of simulation experiments on matrices of known structure, and illustrated in examples drawn from the fields of taxonomy, political science, and data architecture.     

Cross-sectional approach for clustering time varying data

Cluster analysis is to be performed on a three-mode data matrix of type: units, variables, time. A general model for calculating the distance between two units varying in time is proposed. One particular model is developed and used in an example concerned with clustering of 23 European countries according to the similarity of energy consumption in the years 1976–1982.Supported in part by the Research Council of Slovenia, Yugoslavia.     

Evolution of two properties for scale-free network

1 .INTRODUCTIONBecause complex network structure can describe awide variety of systems of high technological andintellectual i mportance ,there has been a consider-able interest in the growth properties of randomnetworks[1 ,2]. The typical random network is ascale-free network, which connectivity distribu-tions behave as power law. Scale-free network isan evolving network, which has many propertiesduring evolution, for example , the fitness andclustering coefficient of node are two i mport…     

基于粗糙集全局离散和粒子群算法的启发式航材订货模型

为了解决目前航材订货模型单纯依托人为经验,对航材消耗规律变化反应滞后且可信度不高工作量过大的问题,提出在原始订货模型的框架下,使用PAM聚类、粗糙集全局离散和粒子群算法,计算模型中的航材分类、属性离散和区间权重,从而建立启发式航材订货模型,并在测试集上对比新老模型的均方离差。结果表明,启发式航材订货模型可以使人摆脱依靠经验确定模型的繁琐工作,并且能有效提高模型的准确性和及时性,从而提高航材订货工作效率。     

基于K均值和支持向量机的燃料电池在线自适应故障诊断

基于K均值(K-means)和支持向量机(support vector machine, SVM)算法,提出了一种车用燃料电池系统(fuel cell system, FCS)在线自适应故障诊断方法.该方法通过不断获取系统最新单体电压,采用K-means算法改进传统的静态SVM分类器模型,对实时获取的信息进行聚类,实现分类器的在线自适应调节.采用已发表文献中的实验数据进行了相关的验证分析,结果表明,提出的方法能有效地在线调节故障分类器,实现FCS系统特性发生改变后的故障检测.