关于数据挖掘应用软件开发中的发展趋势的探索研究

数据、数据挖掘的任务和数据挖掘的方法的多样性对数据挖掘提出了许多挑战性的研究问题.有效的数据挖掘方法、系统和服务的开发,交互的和集成的数据挖掘环境的构建是关键的研究领域.使用数据挖掘技术可以有效地解决大型或者复杂的应用问题是数据挖掘研究人员、数据挖掘系统和应用的开发人员面临的重要任务.     

WEB2.0时代网络隐私保护的数据挖掘方法研究

近年来隐私保护的数据挖掘是数据研究的热点问题之一,并且在此领域取得了丰硕的研究成果.近几年,随着计算机技术的进一步发展,“Web2.0”的概念应运而生,互联网应用以及技术越来越多地被应用到各个领域.目前被广泛应用的移动通信技术、定位技术等等,以及物联网、位置服务、社交网络的出现,更多的涉及到了个人隐私信息,这种情况下,通过数据挖掘工具对数据进行分析很容易攫取个人隐私.针对这种情况,对隐私保护数据挖掘方法进行深入分析对于我国以及全球的互联网安全具有重要的现实意义.     

浅谈数据挖掘技术在金融业的应用和作用

数据挖掘技术的革新为金融机构的发展不断带来了新的契机。笔者以数据挖掘技术在金融业中的应用这一重要议题展开讨论,阐明数据挖掘技术的基本概念,并结合金融业现状,具体分析数据挖掘技术在金融数据风险分析、金融机构关系管理和网络金融交易方面的应用情况。希望能够为普及金融科技知识和尽微薄之力,为金融业技术人员了解数据挖掘技术提供一些参考。     

数据挖掘技术在财务风险预警中的应用

数据挖掘技术能够对财务指标进行准确、全面的分析,提供客观、详细、多方位的数据参考,对构建科学的财务风险控制系统具有重要的意义。鉴于此,本文将对分别对数据挖掘技术应用场景、财务风险指标体系和数据挖掘技术具体方法进行阐述,得到了数据挖掘技术主要以关联规则数据挖掘技术、时间序列数据挖掘技术等模式运用到财务风险预警中的结论,期望对构建科学的企业财务风险预警系统提供有价值的参考。     

数据挖掘在欺诈风险分析中的应用

论述了数据挖掘技术中的决策树、离群数据挖掘、案例推理、粗糙集、神经网络在欺诈风险的分析、识别和评价中具体应用。     

基于遗传算法的数据挖掘技术在数据库营销中的应用

本文首先介绍了面向数据库营销的数据挖掘技术的一般处理流程,接着详细论述了如何设计基于遗传算法的数据挖掘模型,最后通过一个具体的实例来证明这种方法的有效性。     

数据挖掘在企业中的应用

本文简要概述了数据挖掘的基本理论和方法,介绍了数据挖掘在企业中的重要性和基本步骤.     

数据挖掘方法在商业领域的应用研究综述

随着数据采集技术的发展进步,收集到的商业数据信息日渐庞大.如何从众多的数据挖掘方法中选择有效的技术,从这些海量数据中挖掘有用的知识是商业数据分析者或商业决策者面临的艰巨任务.本文调研了数据挖掘在商业领域的主要成功应用案例,归纳、总结和分析了在商业领域中常用的数据挖掘方法,指出了它们各自的使用条件和范围.这些可为商业数据分析者和商业决策者选择恰当的数据挖掘方法来指导他们的工作提供帮助.     

数据挖掘技术在经济统计中的应用

数据挖掘技术是一门专业涉及面广泛的交叉学科.在经济统计中,数据挖掘技术主要有概念分层,关联规则和决策树方法.     

电子商务环境下基于ERM的企业财务风险预警系统构建

本文在分析电子商务环境下企业财务风险新特点的基础上,提出根据全面风险管理理论采用数据仓库和数据挖掘技术构建企业财务风险预警系统,该系统考虑电子商务环境下财务和业务协同等特点,采用各种数据挖掘的算法发现未知的模式预测财务风险。     

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。     

A Proposed Model for Consumer-Based Brand Equity Analysis on Social Media Using Data Mining and Social Network Analysis

Online social networks have expanded their “virtual borders,” making the Internet more like an environment of social interaction than a business tool. However, even before the emergence and expansion of social media, marketing professionals were interested in identifying consumers' perceptions about brands. Thus, operational models have been proposed to facilitate such a task. Those models, however, can be expensive and inconvenient, since the models use questionnaires for data collection. To help overcome this problem, this article proposes a model for brand equity analysis from the consumer perspective expressed in social networks using opinion mining techniques and social network analysis. The application of the proposed model on data collected from Twitter made it possible to analyze five brand equity dimensions: brand awareness, brand loyalty, perceived sentiment, perceived quality, and brand associations. The results reached by the application of the model show that brand equity can be analyzed from data retrieved from virtual social networks, disclosing how consumers perceive brands in such an environment, without using questionnaires, enabling different brands in different contexts. Those data can be analyzed under both objective and replicable criteria for each of the brand equity elements that make up the model.     

Big Data

“Big data” describes technologies that promise to fulfill a fundamental tenet of research in information systems, which is to provide the right information to the right receiver in the right volume and quality at the right time. For information systems research as an application-oriented research discipline, opportunities, and risks arise from using big data. Risks arise primarily from the considerable number of resources used for the explanation and design of fads. Opportunities arise because these resources lead to substantial knowledge gains, which support scientific progress within the discipline and are of relevance to practice as well. From the authors’ perspective, information systems research is ideally positioned to support big data critically and use the knowledge gained to explain and design innovative information systems in business and administration – regardless of whether big data is in reality a disruptive technology or a cursory fad. The continuing development and adoption of big data will ultimately provide clarity on whether big data is a fad or if it represents substantial progress in information systems research. Three theses also show how future technological developments can be used to advance the discipline of information systems. Technological progress should be used for a cumulative supplement of existing models, tools, and methods. By contrast, scientific revolutions are independent of technological progress.     

All That Glitters is Not Gold: Digging Beneath the Surface of Data Mining

This article develops a more comprehensive understanding of data mining by examining the application of this technology in the marketplace. In addition to exploring the technological issues that arise from the use of these applications, we address some of the social concerns that are too often ignored.As more firms shift more of their business activities to the Web, increasingly more information about consumers and potential customers is being captured in Web server logs. Sophisticated analytic and data mining software tools enable firms to use the data contained in these logs to develop and implement a complex relationship management strategy. Although this new trend in marketing strategy is based on the old idea of relating to customers as individuals, customer relationship management actually rests on segmenting consumers into groups based on profiles developed through a firm's data mining activities. Individuals whose profiles suggest that they are likely to provide a high lifetime value to the firm are served content that will vary from that which is served to consumers with less attractive profiles.Social costs may be imposed on society when objectively rational business decisions involving data mining and consumer profiles are made. The ensuing discussion examines the ways in which data mining and the use of consumer profiles may exclude classes of consumers from full participation in the marketplace, and may limit their access to information essential to their full participation as citizens in the public sphere. We suggest more ethically sensitive alternatives to the unfettered use of data mining.     

XML与Web数据挖掘

互联网时代,绝大多数信息都来自于Internet,数据挖掘的对象将更侧重于Web数据挖掘,但是Web页面上的信息并不适合进行数据挖掘。Web数据挖掘存在的种种问题,而XML的出现为解决这些问题提供了契机。     

银行信用卡数据集市系统的设计

重点讨论信用卡数据集市的建立方案和在银行业务中的应用,在分析了现有银行信用卡管理信息系统的现状后,讨论信用卡数据集市系统的设计思想和设计架构,并展望了其在银行信用卡业务中的应用前景     

基于数据挖掘技术的企业客户关系管理(CRM)

由于竞争的全球化、需求的拉动和管理理念的更新,使客户关系管理(CRM)得到产生和发展。面对企业海量的数据,如何从其中发现有价值的知识和规律是企业急需解决的难题。数据挖掘技术为此提供了工具和途径。在了解CRM的概念和框架、数据挖掘的各种技术后,还必须了解数据挖掘在CRM中的应用流程和应用的业务领域。     

Analyzing Massive Data Sets: An Adaptive Fuzzy Neural Approach for Prediction,with a Real Estate Illustration

Drawing useful predictions from vast accumulations of data is becoming critical to the success of an enterprise. Organizations’ databases grow exponentially from transactions with external stakeholders in addition to their own internal activities. An important organizational computing issue is that, as they grow, the databases become potentially more valuable and also more difficult to analyze. One example is predicting the value of residential real estate based on past comparable sales transactions. This is critical to several important sectors of the US economy including the mortgage finance industry and local governments that collect property taxes. The common methodology for dealing with such property valuation is based on multiple regression, although this methodology has been found to be deficient. Data mining methods have been proposed and tested as an alternative, but the results are very mixed. This article introduces a novel approach for improving predictions using an adaptive, neuro-fuzzy inference model, and illustrates its application to real estate property price prediction through the use of comparable properties. Although neuro-fuzzy–based approaches have been found to be effective for classification and estimation in many fields, there is very little existing work that investigates their potential in a real estate context. In addition, this article addresses several common problems in existing studies, such as small sample size, lack of rigorous data sampling, and poor model validation and testing. Our model is tested with real sales data from the assessment office in a large US city. The results show that the neuro-fuzzy model is superior in all of the test scenarios. The article also discusses and refines a unique technique to defining comparable properties to improve accuracy. Test results show very promising potential for this technique in mass appraisal in real estate and similar contexts when used with the neuro-fuzzy model.     

Equity market data, bank failures and market efficiency

The paper examines the informational content of market data for long-term horizons in models, which predict bank failure. Univariate results document patterns such as declining prices, negative returns, declining dividends, and rising return volatility, up to 4 years before failure. Multivariate analysis shows that market information improves the failure predictive content of traditional models, which are based on accounting data. Out-of-sample predictions show that the use of stock market data does improve the forecast of bank failure. Furthermore, the persistence of this contribution generally increases with greater distances from the date of failure documenting the forward-looking nature of financial markets.     

User preference mining based on fine-grained sentiment analysis

User preference mining is an application of data mining that attracts increasing attention. Although most of the existing user preference mining methods achieved significant performance improvement, the sentiment tendencies of users were seldom considered. This paper proposes fine-grained sentiment analysis for preference mining. The powerful feature representation capabilities of deep neural networks have significantly improved the performance of fine-grained sentiment analysis. But two main challenges remain when using deep neural network models: incomplete user feature extraction and insufficient interaction. In response, a pre-training language model is employed to encode user features to fully explore potential interests of users, a linguistic knowledge model is introduced to assist the encoding, a multi-scale convolution neural network is adopted to capture text features at different scales and fully utilize the text information, and the fine-grained sentiment analysis task is modeled as a sequence labeling problem to explore the sentiment polarity of user evaluation. Experiments on a user review data set are used to verify the new approach. Experimental results of precision, recall rate and F1-value show that the proposed approach performs better, and is more effective than baseline models. For example, the F1-value is increased by 4.27% compared to the best performing baseline model. Findings have important implications for research and practice.