数据挖掘在电信行业的应用研究

数据挖掘是一个应用统计学和人工智能等算法进行知识发现的过程。本文介绍了数据挖掘技术在国内电信行业的应用领域,并以客户流失分析作为实例,探讨了数据挖掘的整个应用过程。最后指出国内电信行业在数据挖掘应用中存在的问题。     

数据挖掘在证券业中的应用研究

数据挖掘是从海量的数据中,抽取出潜在的、有价值的知识的过程。本文首先阐述了数据挖掘的概念,分析了数据挖掘在证券业的广阔应用前景,着重探讨了数据挖掘在证券业的具体应用,并进一步展望证券类数据挖掘产品的发展策略与前景。     

浅析数据挖掘在企业CRM中的应用

本文从数据挖掘的本质出发,介绍了在企业CRM中应用数据挖掘技术的必要性,详细叙述了数据挖掘在CRM中的工作过程。     

基于电子商务数据挖掘技术的研究与应用

本文基于电子商务系统,运用数据挖掘技术,研究在电子商务中数据挖掘问题。根据电子商务系统中数据挖掘的特性,阐述了电子商务中数据源、数据的具体挖掘过程、常用数据挖掘技术和应用。     

数据挖掘技术在电子商务中的应用

概述了数据挖掘的定义、方法、过程,论述了数据挖掘技术与电子商务的关系,提出了数据挖掘技术在电子商务系统中应用的体系结构,经过测试,达到了预定的结果。     

基于Multi-Agents技术的区域经济分析的数据挖掘探究

本文阐述了区域经济分析的数据挖掘流程,在充分认识到区域经济系统特征的基础上,提出一个基于Multi-Agents的区域经济分析的数据挖掘应用框架,并采用JADE技术去描述和实现区域经济系统的数据挖掘过程,将区域经济系统抽象成为一个相互协作的Multi-Agents系统,提高经济数据挖掘过程的智能化水平。     

面向商科生的数据挖掘课程教学方法探讨

大数据与云计算的普及对数据挖掘人才提出了更多更高的需求,越来越多的高校开设了面向商科生的数据挖掘课程。然而,数据挖掘是一门理论与实践并重且涉及多学科的交叉学科课程,传统的教学方法在给商科生的授课过程中面临许多问题。结合数据挖掘课程的特点,分析了教学过程中的问题,并提出了建设性的意见。     

浅析计算机领域的数据挖掘技术

随着空间数据采集技术的飞速发展，复杂多样的空间数据日益膨胀，迫切需要更新数据挖掘的知识和方法。文章从空间数据挖掘的基本概念出发，阐述了空间数据挖掘的类型与过程，介绍了空间数据挖掘在GIS中的应用，分析了当前空间数据挖掘面临的问题，并对空间数据挖掘技术的发展进行了展望。     

数据挖掘技术在企业客户关系管理(CRM)中的应用

从大型数据库或数据仓库中提取隐含的、未知的,但又是潜在有用的信息和知识的过程称为数据挖掘的过程。利用数据挖掘技术实施企业CRM系统已经成为当今企业管理中的重点,本文分析了数据挖掘在企业客户关系管理中应用的必要性及所起到的作用,进而提出在客户关系管理系统运行过程中,实施数据挖掘时的具体步骤和挖掘结果的表现形式。     

网络营销中数据挖掘技术的研究

本文主要讨论了数据挖掘技术,重点研究了网络营销中进行数据挖掘的过程、方法及应用等问题。     

数据挖掘技术在零售业中的应用

本文首先对数据挖掘技术在零售业领域应用的意义进行了说明,介绍了数据挖掘的概念、应用的步骤和包含的主要部分,并对零售业数据挖掘技术的具体应用方法进行了较详细的分析。     

Data mining in business services

Data mining applies traditional statistical tools as well as artificial intelligence algorithms to the analysis of large datasets. Data mining has proven very effective in many fields, including business. This paper reviews applications of data mining relevant to the service industry, and demonstrates primary business functions and data mining methods. Typical industry data mining process is described, analytic tools are reviewed, and major software tools noted.     

Customer relationship management in retailing: A content analysis of retail trade journals

The purpose of this research was to increase knowledge and understanding of how retailers use business intelligence and data mining tools to implement customer relationship management (CRM) in retailing. Specific objectives were to (1) identify organization and infrastructure requirements for CRM effectiveness, (2) identify CRM objectives and goals of retail companies, (3) identify data mining tools utilized by retailers to perform CRM functions, and (4) identify CRM strategies used by retail companies. A keyword search within business databases using CRM and CRM identified publications with CRM content. Content analysis was used on articles (N=149) drawn from Stores, Chain Store Age, Harvard Business Review, and Retail Forward over a 5 year period (2000–2005). Selected articles were stored as text files in QDA Miner, a computerized qualitative analysis tool. Key organization/infrastructure needs emerged focusing on data structure, organizational systems, technology structure, and data accessibility. Retailers goals/objectives and strategies focused on marketing, customer service, understanding customers through data analysis and increasing acquisition and retention through customer loyalty programs. Data mining tools identified supported marketing and customer analysis efforts. Findings provide insight into the challenges retailers face as they implement a more customer-centric business strategy.     

以数据挖掘提升SRM决策分析能力

传统供应商关管理(SRM)在供应商分类评价方面存在不足,将数据挖掘技术引入其中,以从大量的数据信息中发现影响供应商合作价值的潜在规律,从而提升SRM的决策分析能力。设计中综合使用了数据仓库、聚类分析、决策树方法,并在国内一大型制药企业运用实践,为SRM的深入开发和应用提供了有价值的参考。     

Data mining in road crash analysis: the context of developing countries

The recent advancements in the field of data mining have made vast progress in extracting new information and hidden patterns from large datasets which are often overlooked by the traditional statistical approaches. These methods focus on searching for new and interesting hypothesis which were previously unobserved. Road safety researchers working with the crash data from developed world have seen encouraging success in obtaining new insight into crash mechanism through data mining. An attempt was made in this study to apply these advance methods and evaluate their performance in manifesting crash causes for Bangladesh. The study applies hierarchical clustering to identify hazardous clusters, random forest to find important variables explaining each of these clusters, and classification and regression trees to unveil their respective crash mechanisms for the road crash data of Bangladesh. The results identified several new interesting relationships and acknowledged issues related to quality of data.     

基于决策树的数据挖掘方法在CRM中的应用研究

基于ID3算法的CRM数据分类实例证明:决策树的核心问题是选择最佳的划分标准,采取数据挖掘中的决策树分类算法,将其应用于CRM系统中,以从中发现企业产品的销售规律和客户群特征,从而提高CRM对市场活动和销售活动的分析能力,以期为企业更科学、更有效地制定产品开发、生产、销售和服务策略提供有力的技术支持。     

A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring

Data mining techniques have numerous applications in credit scoring of customers in the banking field. One of the most popular data mining techniques is the classification method. Previous researches have demonstrated that using the feature selection (FS) algorithms and ensemble classifiers can improve the banks' performance in credit scoring problems. In this domain, the main issue is the simultaneous and the hybrid utilization of several FS and ensemble learning classification algorithms with respect to their parameters setting, in order to achieve a higher performance in the proposed model. As a result, the present paper has developed a hybrid data mining model of feature selection and ensemble learning classification algorithms on the basis of three stages. The first stage, as expected, deals with the data gathering and pre-processing. In the second stage, four FS algorithms are employed, including principal component analysis (PCA), genetic algorithm (GA), information gain ratio, and relief attribute evaluation function. In here, parameters setting of FS methods is based on the classification accuracy resulted from the implementation of the support vector machine (SVM) classification algorithm. After choosing the appropriate model for each selected feature, they are applied to the base and ensemble classification algorithms. In this stage, the best FS algorithm with its parameters setting is indicated for the modeling stage of the proposed model. In the third stage, the classification algorithms are employed for the dataset prepared from each FS algorithm. The results exhibited that in the second stage, PCA algorithm is the best FS algorithm. In the third stage, the classification results showed that the artificial neural network (ANN) adaptive boosting (AdaBoost) method has higher classification accuracy. Ultimately, the paper verified and proposed the hybrid model as an operative and strong model for performing credit scoring.     

Determining profitable products in the retail market with consideration of cash limitation and exhibition periods

In the selection of profitable products, consumer preferences and retailer constraints in products supply must be considered. When data mining algorithms are used to discover the consumer's preferences from transaction database, the results may be biased, if the exhibition period of the products has not be considered. In this study a new method is proposed to adjust the support and confidence coefficients of traditional association rule mining algorithms such as Apriori or FP-growth taking into consideration of common exhibition periods. On the supply side, the retailer may have some limitations in terms of buying and supplying products in the store. In the most recent researches, only the shelf space constraint has been considered. In this study, financing as an important constraint in the retail market and the opportunity cost of money are imported in the selection of profitable products.The researcher's experiment on real world data shows that the number of frequent itemsets increases significantly when products exhibition periods are taken into consideration. In this case, if the retailer considers the opportunity cost of money as 1%, the profitable set composition will be changed by 10%. Also, when the opportunity cost is 1% and the retailer faces cash limitation, the number of products is reduced by 21% in the most profitable set, whereas the new set composition is 29.4% different from the base set.     

Mobile payment and online to offline retail business models

Mobile payments are services that use mobile devices to make payments. When digitalization moves across channel boundaries, online to offline channel retail will expand. Online to offline retailing will become the future retail owner stream and retail operators will move from cross-channel or multi-channel to omni-channel. This study investigates a market survey in Taiwan developing a data mining analytics including clustering analysis and association rules based on a snowflake schema database design. The role of mobile payment is determined in terms of new retail payment mechanism that promotes a better consumer purchase experience in an online to offline business environment.     

Data mining: proprietary rights, people and proposals

This article focuses on the issue of data mining as it relates to the consumer and to the issue of whether the consumer's private information has any proprietary status. A brief review of data mining is provided as a background for a better understanding of the purposes and uses of data mining. Also examined are several issues of the ethics of data mining, including a review of stakeholders, who they are and which may be most seriously affected by unethical data mining practices. Several suggestions for the improvement of data mining as it relates to the consumer are further presented: suggestions that would allow for data mining that would be beneficial to both the business community and the consumer.