实数阶累加运算及其在GM(1,1)模型中的应用

本文考虑实数阶累加运算,从理论上证明了将所有实数阶累加运算看成一个集合,在其上定义普通数的加法与数乘运算后,其为交换群.分析表明先对原序列进行m阶累加,在新得到的序列中再进行1阶累加,等价于对原序列进行m+1阶累加,结果表明对原序列先进行m阶累加,然后再进行-m阶累加,序列就回到原始序列.同时s阶累减运算可看成-s阶累加运算,最后将分析结果用于GM(1,1)模型中,并进行误差分析.     

南四湖人湖河流水质面板数据分析的灰色速率模型

针对面板数据灰色关联决策评价模型的关联度计算问题,在传统的面板数据灰色关联决策评价模型的基础上,构建评价对象的指标时间数据的累加序列,通过指标时间累加生成速率序列构造面板数据灰色生成速率关联决策模型,采用生成速率序列的接近性表征原始数据序列的动态变化趋势.通过灰色累加生成速率关联决策方法扩展到面板数据分析,解决了小样本面板数据的灰色评价分析问题,将这种方法应用于南四湖入湖河流水质面板数据质量评价中,经实例计算验证了面板数据灰色累加生成速率关联决策评价模型的稳定性、合理性和实用性为水环境面板数据质量分析评价提供了可行的计算思路和方法.     

基于粒子群算法的变权累加生成的GM(1,1)模型

根据灰色系统的新信息优先原理可知新信息对认知的作用大于旧信息的作用,而传统的累加生成没有体现原始数据中新信息的重要性.针对这一问题引入了变权累加生成的方法,并对变权累加生成在单调性、灰指数规律、凸性等方面的性质进行了研究,得到变权累加生成序列具有单调递增性,具有较强的指数规律,并具有下凸性,这些性质是高精度建模的保证,然后建立了基于变权累加生成的GM(1,1)模型,并运用粒子群算法确定了变权累加生成的权重.通过具体的算例计算表明,变权累加生成的GM(1,1)模型能够提高模型的模拟和预测精度.     

加权累加生成的GM(1,1)模型及其应用

根据灰色系统理论中的新信息优先原理知新信息对认知的作用大于老信息的作用,而传统的累加生成没有体现原始数据中新信息的重要性.针对这一问题提出了加权累加生成的概念,并对加权累加生成在单调性、灰指数规律、凸性等方面的性质进行了研究,得到加权累加生成序列具有单调递增性,具有较强的指数规律,并具有下凸性,然后建立了基于加权累加生成的GM(1,1)模型.通过具体算例的计算表明,加权累加生成的GM(1,1)模型的模拟和预测精度比传统的GM(1,1)模型模拟和预测精度高,从而说明了该法的有效性.     

分数阶反向累加与累减生成算子及互逆性

采用反向累加与反向累减的信息处理方式,将一阶累加生成算子和一阶累减生成算子拓展至分数阶范围,给出了分数阶反向累加生成算子与分数阶反向累减生成算子的解析表达式,并证明了两者之间的互逆性,为建立基于分数阶反向累加的灰色预测模型和拓宽灰色预测模型的应用范围提供理论基础.     

倒数累加生成灰色GRM(1,1)模型及应用

相对传统的累加生成提出倒数累加生成的定义 ,并给出其灰色 GRM( 1,1)模型及其在药物动力学中的应用 .为建立灰色模型提供了新的生成方法     

分数阶反向累加非线性灰色Bernoulli模型及其应用

非线性灰色Bernoulli模型相对于普通的GM(1,1)模型,能更好的反映数据序列的非线性增长趋势.分数阶蕴含"in between"思想,分数阶累加灰色模型相对一般的累加灰色模型具有更好的预测效果和适应性.为了更好地符合新信息优先原理,实现最小信息的最大挖掘,构造了分数阶反向累加非线性灰色Bernoulli模型,即...     

基于分数阶累加的灰色GM(1,1|sin)模型及其应用

本文研究了灰色模型对振荡序列的预测问题.在已有GM(1,1|sin)模型的基础上,利用分数阶算子对原始序列进行累加生成的方法,获得了分数阶累加GM(1,1|sin)模型的表达式;以平均相对误差最小化为目标,利用粒子群算法求解非线性优化问题,获得了模型的最优参数.最后以城市交通流的模拟预测为例,结果表明本文提出的模型比GM(1,1|sin)模型具有更高的模拟精度,推广了GM(1,1|sin)预测模型的结果.     

分数阶灰色累加生成算子与累减生成算子及互逆性

针对现有灰色预测模型主要以一阶累加生成序列作为建模序列,再累减还原为原始序列预测值,本文通过Gamma函数将累加生成算子和累减生成算子拓展到正实数领域,给出分数阶累加生成算子和分数阶累减生成算子的解析表达式,一阶和整数阶均是其特例,证明了两算子之间的互逆性.为建立分数阶灰色预测模型和拓宽灰色预测模型的应用范围提供理论基础.     

分数阶反向累加非等间距GM(1,1)模型及应用

针对非等间距递减序列的预测问题,首先构建了一阶反向累加非等间距GM(1,1)模型(简称为非等间距GOM(1,1)模型),并给出了模型参数的最小二乘解和可用于预测的离散时间响应式.为进一步提高模拟预测精度,利用分数阶累加思想,提出了分数阶非等间距GOM(1,1)模型.以平均模拟相对误差最小化为目标,建立非线性规划模型可求解得到最优阶数.最后,以数值模拟和钛合金疲劳强度随温度变化预测为例,证实了该文提出模型的有效性和实用性.     

s-CorrPlot: An Interactive Scatterplot for Exploring Correlation

The degree of correlation between variables is used in many data analysis applications as a key measure of interdependence. The most common techniques for exploratory analysis of pairwise correlation in multivariate datasets, like scatterplot matrices and clustered heatmaps, however, do not scale well to large datasets, either computationally or visually. We present a new visualization that is capable of encoding pairwise correlation between hundreds of thousands variables, called the s-CorrPlot. The s-CorrPlot encodes correlation spatially between variables as points on scatterplot using the geometric structure underlying Pearson’s correlation. Furthermore, we extend the s-CorrPlot with interactive techniques that enable animation of the scatterplot to new projections of the correlation space, as illustrated in the companion video in supplementary materials. We provide the s-CorrPlot as an open-source R package and validate its effectiveness through a variety of methods including a case study with a biology collaborator. Supplementary materials for this article are available online.     

社会调查中二分数据的转换与解读——以《2005年中国青少年网瘾调查报告》中的数据分析为例

社会调查中常常使用尽可能简短的问卷,为此将会收集到大量的二分数据。本文讨论了产生二分数据的两种主要形式,探讨了如何在遵循统计分析程序的基本要求下,对二分变量进行转换和解读,从中挖掘出丰富而深入的信息,并以《2005年中国青少年网瘾调查报告》中的数据分析为例进行了说明。     

基于粗糙集理论的智能数据分析模型

粗糙集理论作为一种智能数据分析和数据挖掘的新的数学工具,其主要优点在于它不需要任何关于被处理数据的先验或额外知识.提出了一种基于粗糙集理论的智能数据分析模型,从目标数据集出发,通过数据预处理、数据分类和规则获取,实现对原始数据集的智能分析,并通过实例测试验证了该模型的有效性.     

微观数据和宏观汇总数据在统计分析上的差异——以C-D生产函数为例

基于2008年经济普查的数据,从描述统计分析和回归分析两方面分别对微观数据和宏观汇总数据在统计分析上的差异进行了实证分析.在描述统计分析中发现,宏观汇总数据比微观数据更接近正态分布,但对数化处理后的数据并非如此;在回归分析中发现,基于微观数据和宏观汇总数据估计的生产函数,在消除异方差和多重共线性之前,无论是在生产函数的规模效应、生产要素的贡献率以及生产要素对产出的解释力度上均存在着差异,但是在消除异方差和多重共线性之后,在要素对产出的解释力度上仍存在很大差异.     

Biclustering Three-Dimensional Data Arrays With Plaid Models

Three-dimensional data arrays (collections of individual data matrices) are increasingly prevalent in modern data and pose unique challenges to pattern extraction and visualization. This article introduces a biclustering technique for exploration and pattern detection in such complex structured data. The proposed framework couples the popular plaid model together with tools from functional data analysis to guide the estimation of bicluster responses over the array. We present an efficient algorithm that first detects biclusters that exhibit strong deviations for some data matrices, and then estimates their responses over the entire data array. Altogether, the framework is useful to home in on and display underlying structure and its evolution over conditions/time. The methods are scalable to large datasets, and can accommodate a variety of dynamic patterns. The proposed techniques are illustrated on gene expression data and bilateral trade networks. Supplementary materials are available online.     

数据挖掘过程中连续属性离散化新方法研究

在知识发现和机器学习领域里,许多数据挖掘方法如基于粗集的数据挖掘工具等需要使用离散的属性值,但实际观测到的大多是连续性属性数据,这对许多新型数据挖掘工具的研究带来了不便.本文针对以上问题,在综合分析目前连续属性离散化方法的基础上,提出了一种基于数据分布特征的连续属性离散化新方法,并用经典算例验证了此算法,实验结果表明该方法具有合理性和可行性.     

Rejoinder on “A distance-based statistical analysis of fuzzy number-valued data”

This note is a rejoinder on our paper in this issue. It attempts to provide some clarifications and thoughts in connection with the discussions/comments made about it by Didier Dubois and Sébastien Destercke. We hope our comments are at the level of the discussants'.     

Prospecting for gems in credit card data

After a brief high-level discussion of the nature of data mining,this paper describes some of our experiences in mining creditcard transaction data. We illustrate the use of simple graphicaltools for unearthing unexpected patterns and relationships,and show how more sophisticated modelling can build on suchdiscoveries.     

Pixel-Oriented Visualization Techniques for Exploring Very Large Data Bases

Abstract

An important goal of visualization technology is to support the exploration and analysis of very large amounts of data. This article describes a set of pixel-oriented visualization techniques that use each pixel of the display to visualize one data value and therefore allow the visualization of the largest amount of data possible. Most of the techniques have been specifically designed for visualizing and querying large data bases. The techniques may be divided into query-independent techniques that directly visualize the data (or a certain portion of it) and query-dependent techniques that visualize the data in the context of a specific query. Examples for the class of query-independent techniques are the screen-filling curve and recursive pattern techniques. The screen-filling curve techniques are based on the well-known Morton and Peano–Hilbert curve algorithms, and the recursive pattern technique is based on a generic recursive scheme, which generalizes a wide range of pixel-oriented arrangements for visualizing large data sets. Examples for the class of query-dependent techniques are the snake-spiral and snake-axes techniques, which visualize the distances with respect to a data base query and arrange the most relevant data items in the center of the display. In addition to describing the basic ideas of our techniques, we provide example visualizations generated by the various techniques, which demonstrate the usefulness of our techniques and show some of their advantages and disadvantages.     

DETERMINATION OF PERMANENT OPTIMAL DATA POINTS AND AN EFFICIENT ALGORITHM FOR LAD PROBLEM

This paper gives a definition of permanent optimal data point of Least Absolute Deviation(LAD)problem.Some theoretical results on non-degenerate LAD problem are obtained.For computing LAD problem,an efficient,algorithm is given according to the idea of permanent optimal data point.Numerical experience shows that our algorithm is better than many of others,including the famous B R algorithm.