Extending ER models to capture database transformations to build data sets for data mining

In a data mining project developed on a relational database, a significant effort is required to build a data set for analysis. The main reason is that, in general, the database has a collection of normalized tables that must be joined, aggregated and transformed in order to build the required data set. Such scenario results in many complex SQL queries that are written independently from each other, in a disorganized manner. Therefore, the database grows with many tables and views that are not present as entities in the ER model and similar SQL queries are written multiple times, creating problems in database evolution and software maintenance. In this paper, we classify potential database transformations, we extend an ER diagram with entities capturing database transformations and we introduce an algorithm which automates the creation of such extended ER model. We present a case study with a public database illustrating database transformations to build a data set to compute a typical data mining model.     

数据挖掘在客户关系管理中的应用

利用标准化客户数据，确定了聚类相似度公式和评价指标，使用层次凝聚方法和K-平均算法实现了客户的自动聚类；并且在权衡算法效率和聚类精度的基础之上提出了改进的聚类距离公式和K-平均算法，达到了较好效果。     

Toward a hybrid data mining model for customer retention

The prevention of subscriber churn through customer retention is a core issue of Customer Relationship Management (CRM). By minimizing customer churn a company maximizes its profit. This paper proposes a hybridized architecture to deal with customer retention problems. It does so not only through predicting churn probability but also by proposing retention policies. The architecture works in two modes: learning and usage.

In the learning mode, the churn model learner seeks potential associations from the subscriber database. This historical information is used to form a churn model. This mode also calls for a policy model constructor to use the attributes identified in the churn model to divide all ‘churners’ into distinct groups. The policy model constructor is also responsible for developing a policy model for each churner group. In the usage mode, a churn predictor uses the churn model to predict the churn probability of a given subscriber. When the churn model finds that the subscriber has a high churn probability the policy model is used to suggest specific retention policies.

This study’s experiments show that the churn model has an evaluation accuracy of approximately eighty-five percent. This suggests that policy model construction represents an interesting and important technique in investigating the characteristics of churner groups. Furthermore, this study indicates that understanding the relationships between churns is essential in creating effective retention policy models for dealing with ‘churners’.     

基于消费数据挖掘的多指标客户细分新方法

提出一种过程完整的针对消费数据挖掘的客户细分新方法。设计了包含3种类型10个指标的客户细分模型, 并采用因子分析法从中提取细分变量, 再使用基于划分的聚类算法进行客户细分。通过对某大型纸巾生产企业100万销售数据的计算分析, 得出了有效客户类别, 表明了本方法具有更强的客户细分能力和客户行为特征的解释能力。     

基于数据挖掘的客户细分方法的研究

客户细分是客户关系管理中基础的、重要的内容。全面考虑了客户生命周期价值,基于群体决策技术和数据挖掘技术提出了一种新的客户细分方法。在群体决策的基础上,确定影响客户细分的变量,利用层次分析法,确定各个变量的权重。利用数据挖掘的聚类技术,进行客户细分。用某橡胶企业的数据进行了验证,结果表明,该方法能够有效地支持企业的客户细分,为企业的决策提供依据。     

数据挖掘中关联规则挖掘算法比较研究

分析数据挖掘中关联规则挖掘算法的研究现状,提出关联规则新的价值衡量方法和关联规则挖掘今后进一步的研究方向。以核心Apfiofi算法为基点,运用文献查询和比较分析方法对典型的关联规则挖掘算法进行了综合研究：Apfiofi法即使进行了优化,一些固有的缺陷仍然无法克服,还需进一步研究;②今后的研究方向将是提高处理极大量数据和非结构化数据算法的效率、与OLAP相结合以及生成结果的可视化。     

Protecting business intelligence and customer privacy while outsourcing data mining tasks

Nowadays data mining plays an important role in decision making. Since many organizations do not possess the in-house expertise of data mining, it is beneficial to outsource data mining tasks to external service providers. However, most organizations hesitate to do so due to the concern of loss of business intelligence and customer privacy. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules, at the same time, protect their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off the storage requirement. This research was supported by the USA National Science Foundation Grants CCR-0310974 and IIS-0546027.

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基于数据挖掘的客户细分模型研究及应用

为企业更深入了解消费者的行为和偏好,帮助企业制定决策和发展客户关系,结合现有的客户细分方法,提出一种多指标客户细分模型。从宏观和微观角度,对传统指标进行优化,构建RFMPA多指标客户体系;采用熵值法客观赋权;采用因子分析降维;采用改进的K-means算法完成客户细分。利用大型连锁超市客户消费数据进行实证研究,对比数据实验结果表明,该模型能够更好解决客户细分问题,提高企业客户关系管理和决策质量。     

一种新的重名消解算法在保险领域中的应用研究*

研究客户重名消解问题。针对以往重名消解方法如文本聚类的方法需考虑大量无用词汇并需要人工设定阈值以及类别数量,而基于信息抽取的人物相关属性相似度方法对于人物信息的抽取具有依赖性,提出了一种改进的重名消解算法。该算法首先对具有相同标志的客户进行属性匹配,合并匹配成功的标志;然后进行链接分析,对客户合作网的结构进行分析,将具有相同标志并与同一个代理人实体合作的客户归为一个客户实体,并把具有相同合作对的信息加以分析合并;最后通过原子团簇分析法进行聚类分析。仿真实验结果表明,所提改进算法对中文字符串的匹配处理进行了优化,执行效率高,适合于以大量数据为特征的保险领域的重名消解。     

Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution

Information Technology and Management - This paper proposes a data mining approach for automatic customer targeting based on their expected profitability. The main challenge with customer...     

利用Web Services技术构建开放式数据挖掘系统

提出了一种新颖的数据挖掘系统的体系结构,该结构把SOA与传统的挖掘系统结构相结合.在此体系结构的基础上,实现了一个开放式挖掘系统,能够动态集成挖掘算法.将该系统应用于福州地热资源的数据挖掘中,结果证明通过将WebServices技术引入数据挖掘系统的构建中,能大大增强挖掘系统的功能.     

Using data mining technique to enhance tax evasion detection performance

Currently, tax authorities face the challenge of identifying and collecting from businesses that have successfully evaded paying the proper taxes. In solving the problem of tax evaders, tax authorities are equipped with limited resources and traditional tax auditing strategies that are time-consuming and tedious. These continued practices have resulted in the loss of a substantial amount of tax revenue for the government. The objective of the current study is to apply a data mining technique to enhance tax evasion detection performance. Using a data mining technique, a screening framework is developed to filter possible non-compliant value-added tax (VAT) reports that may be subject to further auditing. The results show that the proposed data mining technique truly enhances the detection of tax evasion, and therefore can be employed to effectively reduce or minimize losses from VAT evasion.     

An efficient data mining approach for discovering interesting knowledge from customer transactions

Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules and sequential patterns from a large database, and users may be only interested in some information.

Moreover, the criteria of the discovered association rules and sequential patterns for the user requirements may not be the same. Many uninteresting information for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the association rules or sequential patterns to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules and the sequential patterns according to the user requirements.     

Application of data mining methods to efficient microwave active device modeling

In this work, the signal and noise behaviors of a microwave transistor within its operation domain (voltage drain to source–V_DS, current of drain to source—I_DS, frequency—f) are modeled by data mining techniques (DMT) without using any information on the microwave circuit theory. The device is modeled by a black box whose small signal (S) and noise parameters are evaluated through data mining techniques, based on the fitting of both of these parameters for multiple bias and configuration. It has been shown that DMT have a high potential of faithful and efficient device modeling. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Why build a customer relationship management capability?

The market enthusiasm generated around investment in CRM technology is in stark contrast to the naysaying by many academic and business commentators. This raises an important research question concerning the extent to which companies should continue to invest in building a CRM capability. Drawing on field interviews and a survey of senior executives, the results reveal that a superior CRM capability can create positional advantage and subsequent improved performance. Further, it is shown that to be most successful, CRM programs should focus on latent or unarticulated customer needs that underpin a proactive market orientation.     

数据挖掘技术在电信客户流失预警系统中的研究

在明确业务问题的基础上,筛选有效的输入数据和目标变量,并对输入变量各参数之间以及输入变量与目标变量之间进行相关性分析,选取有效的参数。在数据准备完成的基础上,利用Neural Network来建立预测模型,并给出预测结果,通过运行实际业务中的数据对模型进行评估。通过该模型预测可能流失的客户,并给出预警信号,以便企业做出经营决策,挽留有关用户,确保企业效益不受影响。     

数据挖掘中聚类算法的新发展*

在对传统聚类方法进行简要介绍的基础上,对聚类的新发展进行了较详细的归纳,总结了聚类分类方法发展的趋势。     

Integrating AHP and data mining for product recommendation based on customer lifetime value

Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers’ needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.