数据挖掘在人口学中的应用

随着收集数据能力的加强,人口数据和资料日益丰富起来,但同时又普遍感到存在着"数据丰富而信息匮乏"的问题.试图将数据挖掘的技术应用到第五次人口普查当中,通过具体的例子分析来说明在人口学领域如何运用数据挖掘技术,以此探讨如何对五普的数据进行深入分析以及如何提高其应用价值.     

商业企业营销数据挖掘及营销数据统计分析系统的有效构建

当前,商业企业的业务应用信息系统建设已经普遍取得了一定的成效,并且已存有大量数据资源,如何有效利用这些数据信息,成为营销数据统计分析人员需要关注的一个问题.基于此,本文首先根据大量实际资料总结了数据挖掘技术的概念及其发展现状,然后对一些常用的数据挖掘技术做了介绍,最后重点阐述了如何应用相关数据挖掘技术有效构建营销数据统计分析系统,以使商业企业在市场竞争中的核心竞争力得到不断增强,最终持续有效地提高商业企业的经济效益.     

应用数据挖掘技术助力商业决策

先进的商业管理理念离不开技术的支持。面对企业激增的海量数据,应运而生的数据挖掘技术可使商业决策的制定更及时、更准确。本文从数据挖掘的功能、典型商业应用、管理流程及所面临的问题等方面讨论如何应用数据挖掘技术助力商业决策。     

大数据时代数据挖掘在管理会计中的应用探讨

科学技术的不断发展,极大地促进了大数据技术的飞跃,使其加快了向社会生产生活方方面面的渗透进程。在大数据背景下,如何将数据挖掘技术更加充分地应用于管理会计工作实践,成为业内广泛关注的焦点问题之一。基于此,首先介绍了数据挖掘技术的特点及应用流程,分析了大数据时代数据挖掘技术对管理会计的影响,并结合相关实践经验,分别从作业成本和价值链分析等多个角度与方面,就数据挖掘技术在管理会计中的应用展开了探讨,望有助于相关工作的实践。     

浅谈数据挖掘技术在企业客户关系管理的应用

随着信息技术中的数据挖掘技术的不断成熟与发展,金融企业利率市场化进程的快速推进,企业面临着巨大的互联网金融的冲击和挑战。如何能更好的运用数据挖掘技术,使金融企业摆脱困境,走出低谷具有十分重要的应用价值和现实意义。特别是,在现阶段金融企业不断开放,很多企业都大力提倡和发展以客户为中心的客户关系管理体系。在管理和发展的过程中,势必会产生大量的数据,如何结合数据挖掘技术有效的协调和处理客户数据,挖掘潜在的、有用的数据信息,以此来解决客户关系管理中所遇到的问题,为企业的发展提供有效的决策依据和手段,帮助金融企业高效率的维护企业信息的同时,带来更多的经济效益。本文从数据挖掘与客户关系管理两者的关系出发,深入浅出的分析银行金融企业在客户关系管理应用数据挖掘存在的诸多问题,针对问题如何运用数据挖掘技术在银行金融企业扭转被动局面,提出应用方法和解决措施。从而提高客户关系管理体系的构建,为企业实现利润最大化。     

基于数据挖掘技术的公司财务管理策略分析

如何提高管理的专业性和安全性,现已经成为了当代公司重点关注的问题,越来越多的公司选择在财务管理中应用数据挖掘技术,以此来满足日渐增多的财务管理要求。为此,首先阐述了数据挖掘的定义及方法,其次从财务信息利用率、财务管理工作效率、公司财务数据挖掘要求等方面分析了数据挖掘技术在公司财务管理中的应用价值,最后从大数据云处理平台、财务风险控制体系、公司盈利能力、公司发展方向、财务数据安全、数据挖掘技术功能、筹资投资及资产管理等方面总结了基于数据挖掘技术的公司财务管理策略,以期为数据挖掘技术在公司财务管理中的应用提供参考。     

基于数据挖掘技术的统计工作创新研究

统计工作在社会服务与企业发展中都发挥着重要作用,随着信息技术的快速发展,大数据技术为统计工作带来了新的机遇和挑战,面对这种环境如何发挥大数据优势,应用数据挖掘技术做好统计工作是相关部门亟待思考的一个问题。下面论文以此为基础探讨数据挖掘技术下统计工作的创新路径。     

数据挖掘技术在管理会计中的应用分析

数据挖掘作为信息化环境中发展的新技术,是从大量的数据中发现和提取有效信息和知识的过程,对于信息的深入加工和科学利用方面具有强大的功能。管理会计在随着时代的发展过程中,不仅需要理论的发展推动,还需要管理技术和方法的不断创新。将数据挖掘技术运用到管理会计中,将有效提高企业的市场竞争力。文章将通过介绍数据挖掘技术的主要应用方法,指出在管理会计中数据挖掘技术的重要意义,分析和探讨数据挖掘技术如何在管理会计领域中进行应用。     

浅析如何在电子商务中应用数据挖掘技术

进入当代信息社会,网络技术、数据库技术飞速发展,全球传统商务正不断朝电子商务衍变。文章通过介绍数据挖掘技术,对数据挖掘技术实际应用展开探讨,旨在为相关人员基于数据挖掘技术、数据挖掘技术实际应用的如何在电子商务中应用数据挖掘技术研究适用提供一些思路。     

谈数据挖掘技术在零售业中的应用

本文主要研究了数据挖掘技术及其在零售业领域的具体应用,深入探讨了数据挖掘技术和数据挖掘技术在零售业领域的应用形式及我国零售企业如何利用数据挖掘技术提高自身竞争力,在激烈的市场竞争中立于不败之地。     

谈谈国富与民富、先富与共富的一些问题

本文认为,把"国富"与"民富"对立和并提是缺乏科学依据的,"国富"与"民富"之间并非完全互相排斥,而是矛盾统一的关系。就"国富"来说,经过改革开放,我国的经济实力也就是"国富"大大增强,但人均GDP不是很高,不能说国家已经很富;就"民富"来说,国民有富有穷,不能一概而论。我国从未明确宣布或实行过所谓"国富优先"的政策,倒是明确宣布并实行了"让一部分人先富起来"的政策。只是多年的实践证明,"让一部分人先富"的目标在很短的历史时期内迅速完成,而"先富带后富,实现共同富裕"的目标却迟迟没有自动实现。文章指出,当前我国收入分配领域最核心的问题不是"国富"与"民富"的矛盾,而是贫富差距急剧扩大与两极分化趋势日益明显的矛盾。克服和扭转贫富差距扩大和两极分化的趋势,不是将"国富优先"转变为"民富优先",而是要明确宣布"让一部分人先富起来"的政策已经完成任务,将之转变为逐步"实现共同富裕"的政策,完成"先富"向"共富"的过渡;要从所有制结构,从强化公有经济为主体、国有经济为主导着手加以解决,只要保持公有制和按劳分配为主体,贫富差距就不会发展到两极分化的程度,从而最终向共同富裕的目标前进。     

河南省土地利用变化及分布格局

利用土地详查数据和2000年河南省土地利用数据,对河南省1999-2005年土地利用变化进行分析。结果表明:受自然、人口、经济和政策等的影响,河南省近七年来土地利用中耕地面积减少,园地、林地、居民工矿用地和交通用地面积增加。据此,对河南省土地利用进行分区,并对几个典型区空间格局进行分析。     

Gauging the Financial Capability of Americans

There is an increasing interest in the economic and financial knowledge of the public. Former Federal Reserve Chairmen Greenspan and Bernanke often stressed the importance of improved economic and financial knowledge of the general public. Recent survey results from the 2012 National Financial Capability Study provide a rich source of information on the state of financial capability of Americans. In this paper we examine the actual knowledge of individuals, investigate how they self-assess their financial capability and knowledge, and analyze the way these relate to financial outcomes. We also examine the way these measures relate to demographic variables. We find that there is room for improvement in financial knowledge, decisions, and capability, which varies among demographic groups and suggest measures for such improvement.     

Predicting direct marketing response in banking: comparison of class imbalance methods

Customers’ response is an important topic in direct marketing. This study proposes a data mining response model supported by random forests to support the definition of target customers for banking campaigns. Class imbalance is a typical problem in telemarketing that can affect the performance of the data mining techniques. This study also contributes to the literature by exploring the use of class imbalance methods in the banking context. The performance of an undersampling method (the EasyEnsemble algorithm) is compared with that of an oversampling method (the Synthetic Minority Oversampling Technique) in order to determine the most appropriate specification. The importance of the attribute features included in the response model is also explored. In particular, discriminative performance was enhanced by the inclusion of demographic information, contact details and socio-economic features. Random forests, supported by an undersampling algorithm, presented very high prediction performance, outperforming the other techniques explored.     

会计信息的数据挖掘方法与银行信贷风险预测

基于信息熵理论和数据挖掘技术,文章提出一种会计信息的数据挖掘方法,投资者和债权人可用这种方法从企业的会计数据中挖掘出决策有用信息.本文以银行信贷风险预测模型构建为例,对这种方法的有效性进行了实证检验.     

A Psychographic Examination of Environmental Orientations

ABSTRACT

As concern about the environment increases, research is needed to help public policy marketers more fully understand and respond to the various aspects of this phenomenon. Of particular interest is (1) whether there exists a multiplicity of approaches to environmental concern and (2) whether it is possible to provide a rich multivariate explanation of each approach. This research goes beyond the often used univariate, demographic basis for describing environmentalism by undertaking a multivariate, psychographic examination of environmentalism. Responses to environmental activity, interest and opinion statements were gathered from 246 adult repsondents. The data were factor analyzed, resulting in five factors, each yielding a rich psychographic interpretation. The study provides unique, multidimensional insight into the manner in which each orientation is environmentally concerned. The study goes on to suggest ways in which public policy marketers can reach each of the environmental orientations and themes to which each is likely to respond.     

Estimating Discrete Joint Probability Distributions for Demographic Characteristics at the Store Level Given Store Level Marginal Distributions and a City-Wide Joint Distribution

This paper provides a solution to the problem of estimating a joint distribution using the associated marginal distributions and a related joint distribution. The particular application we have in mind is estimating joint distributions of demographic characteristics corresponding to market areas for individual retail stores. Marginal distributions are generally available at the census tract level, but joint distributions are only available for Metropolitan Statistical Areas which are generally much larger than the market for a single retail store. Joint distributions over demographics are an important input into mixed logit demand models for aggregate data. Market shares that vary systematically with demographics are essential for relieving the restrictions imposed by the Independence from Irrelevant Alternative property of the logit model.We approach this problem by formulating a parametric function that incorporates both the city-wide joint distributional information and marginal information specific to the retail stores market area. To estimate the function, we form moment conditions equating the moments of the parametric function to observed data, and we input these into a GMM objective. In one of our illustrations we use four marginal demographic distributions from each of eight stores in Dominicks Finer Foods data archive to estimate a four dimensional joint distribution for each store. Our results show that our GMM approach produces estimated joint distributions that differ substantially from the product of marginal distributions and emit marginals that closely match the observed marginal distributions. Mixed logit demand estimates are also presented which show the estimates to be sensitive to the formulation of the demographics distribution.The views expressed are not purported to reflect those of the United States Department of Justice     

Regional and Categorical Patterns in Consumer Behavior: Revealing Trends

We offer a method of analysis that allows for an “unbundling” of the data to a disaggregate household level, and then “rebundling” it in a manner designed to identify patterns and relationships which are otherwise masked. Applying the method in the context of ‘healthy’ products and using census block group level data, we study consumption over several categories in two locations. The analyses show that studies involving geographically dispersed data must test for, and take into account when required, conceptually sound spatial effects in order to accurately assess impact. We also show that while both location and the product category have a significant impact on the proportion of healthy products purchased, the degree to which consumers choose healthier alternatives is a function of the category as well as the location. Finally, we provide preliminary evidence from survey data that supports the variations we find, and further explores attitudinal differences as well. There are rich implications for retailers in that new products introduced to benefit from popular trends (such as ‘healthy’ alternatives) may not succeed for all categories or locations. Retailers would benefit from understanding the spatial, demographic and attitudinal effects that play into consumption behavior, and such effects can be better understood when studying choice at the category and region level. Finally, public policies aimed at promoting healthier purchasing habits may have greater impact if special attention is given to specific categories and regions.     

基于关联规则挖掘电子商务网站数据采集

关联规则挖掘在Web站点中的应用目的是为了发现不同商品在交易过程中的潜在联系,这些规则反应了顾客购买行为习惯,可以用来指导商家科学地安排进贷、库存以及贷架设计等。在本文中以网上书店为例探讨如何规划网站功能,规划数据库,如何采集用户信息以及用户在访问站点时的浏览记录和消费记录。     

Health and Life Sciences as Drivers for Regional Development and Prosperity in the Baltic Sea Region

This article describes the role of health and life sciences as drivers for regional development and prosperity in the Baltic Sea Region. The rapid development of new technologies in combination with steadily increasing data mining capabilities revolutionizes prediction, prevention, and treatment of illnesses. Together with the demographic change, this will challenge the sustainability of regional and national health systems across the BSR. Furthermore, globalization potentially leads to major threats to the health of citizens by pandemic incidents. Due to their dimension and the fragmentation of health care systems and markets, these challenges can be met only with new, multidimensional, cross-sectoral, and innovative approaches involving a broad range of actors and stakeholders. Furthermore, there is a growing need to incorporate health concerns and innovation issues into all policy levels. For the Baltic Sea Region this is both a challenge and an economic opportunity.