Application of data mining techniques in customer relationship management: A literature review and classification

Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000–2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.     

A framework for user driven data management

Scientists within the materials engineering community produce a wide variety of data, with datasets differing in size and complexity. Examples include large 3D volume densitometry files (voxel) generated by microfocus computer tomography (μCT) and simple text files containing results from a tensile test. Increasingly, there is a need to share this data as part of international collaborations. The design of a suitable database schema and the architecture of a system that can cope with the varying information is a continuing problem in the management of heterogeneous data. We present a model flexible enough to meet users’ diverse requirements. Metadata is held using a database and its design allows users to control their own data structures. Data is held in a file store which, in combination with the metadata, gives huge flexibility. Using examples from materials engineering we illustrate how the model can be applied.     

A review on time series data mining

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and continuous nature, is always considered as a whole instead of individual numerical field. The increasing use of time series data has initiated a great deal of research and development attempts in the field of data mining. The abundant research on time series data mining in the last decade could hamper the entry of interested researchers, due to its complexity. In this paper, a comprehensive revision on the existing time series data mining research is given. They are generally categorized into representation and indexing, similarity measure, segmentation, visualization and mining. Moreover state-of-the-art research issues are also highlighted. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation.     

Network resource management mechanisms in SDN enabled WSNs: A comprehensive review

Wireless technologies usually have very limited computing, memory, and battery power that require the optimal management of network resources to increase network performance. The optimization of these network resources provides an efficient network topology, traffic control, routing, and data aggregation. This study presents a qualitative and quantitative investigation to evaluate the efficient network resource management mechanisms for software defined wireless sensor networks (SDN-enabled WSNs) from the beginning of network design to reliable data delivery. In this paper, a taxonomy of network resource management research studies is proposed. A detailed analysis of SDN-enabled WSNs architecture, SDN controllers, topology discovery, routing approaches, flow rules installation, and data aggregation is also discussed. Furthermore, the comparative analysis of resource provisioning methods along with various simulation tools is presented. Moreover, this review outlines open research challenges and prospective future directions for network resource management in SDN-enabled WSNs.     

数据挖掘技术在药物配方研究中的应用

在新型药物的研制过程中,了解药理及其副作用是十分重要的。通过应用数据挖掘技术对生物碱杀螺剂实验数据的分析处理,提出了一种新的多因素模型推导方法,揭示了药物杀螺过程中各个影响因素的变化规律,给出了生物碱杀螺剂在实际应用中的最优参考条件,为合理利用生物碱杀螺剂进行最优效果的杀螺提供了理论依据。     

Applications of Bayesian belief networks in water resource management: A systematic review

Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools.     

基于SQL Server 2005的高校人力资源数据挖掘的研究

介绍了SQL Server2005数据挖掘平台的优势和功能,提出了利用SQL Server2005数据挖掘对高校人力资源数据源中的数据进行挖掘的方法,论述了挖掘模型对高校人力资源管理的支持。     

数据挖掘技术在高校人力资源管理中的应用研究

针对一个真实的高校人力资源数据集,分析了在高校人力资源管理中适用的数据挖掘技术与过程,通过探索性的数据分析进行了特征值的离散化和特征值的归约、特征选择和构造等方面的分析,并给出了衡量教学科研人员科研能力水平的分类标签建议。利用决策树模型分析了影响教师科研能力的几个关键因素,聚类分析对教师的现状进行了客观而有效地描述,关联规则技术描述了教学、科研和社会工作等几方面的关系。研究分析的结果具有较好的解释性。     

Operations research and knowledge discovery: a data mining method applied to health care management

The exponential growth of databanks creates opportunities to expand Operational Research. An example is the development of scientific approaches to "mine" intelligently the huge databanks that complex systems rely on for their management. The contribution presents an approach to exploit a health insurance databank to evaluate the performance of cardiovascular surgery nation wide.     

数据挖掘中自我学习算法研究

随着我国现代化的迅速发展,伴随着快速发展的脚步其数据也越来越多,如何处理这些数据成为了越来越受人关注的问题。因为大数据多,种类复杂的特征,使得数据挖掘越来越重要,而自我学习可以对数据进行分析,并找出其相关模式,因此在商业领域应用广泛。本文主要就是研究数据挖掘中的自我学习算法,了解自我学习算法的特征,并分析在实际中可以应用到哪些领域。     

基于分类挖掘的网格资源分配研究

根据用户访问网格资源的历史信息,采用分类算法对此信息进行挖掘,得出用户使用集群资源的访问规则和模式,在此基础上构造一种基于分类挖掘的资源调度模型、用户调度UA算法以及资源调度CDMRA算法,分别将用户请求调度到各个集群中闲置的CPU资源.实验证明,采用基于分类挖掘的资源分配策略相比其他算法可以减少资源分配过程中对资源的重新分配次数,可以提高网格资源的利用率.     

WEB数据挖掘在电子商务中的研究应用

WEB数据挖掘旨在从大量的WEB数据信息中发现有用的模式和隐藏的信息,从而为决策者提供决策支持,优化市场策略,有效地解决当今互联网信息膨胀的问题。WEB数据挖掘的一个重要应用就是电子商务。电子商务是一个基于网络平台的现代化的商业模式,目前电子商务发展势头强劲,WEB数据挖掘在电子商务中必定有广阔的应用前景。本文将WEB数据挖掘与电子商务相结合,介绍了在电子商务平台中进行WEB数据挖掘的方法,从而为电子商务从业人员提供借鉴,以便更好地分析数据间的隐藏关系和模式,掌握用户喜好,为电子商务平台的市场决策提供决策支持,减少风险。     

A documentation infrastructure for the management of data mining projects

Effective project management is a key factor for successful Knowledge Discovery in Databases (KDD) projects. The systematic documentation of previous knowledge, experiments, data and results is a helpful means of keeping track of the project current status. Despite its value, documentation is most often perceived as an overhead. We propose a documentation infrastructure composed of a documentation model and a supporting environment that allows the capture, storage and retrieval of KDD process-related information and artifacts. The paper describes this infrastructure, and reports preliminary experiences on its use. Preliminary results reveal generalized satisfaction with regard to infrastructure expressiveness and functionality, and highlight the contributions of the documentation produced for improving project management, project execution and team communication. The role of documentation in learning and reuse was also identified.     

A review of current research in manufacturing shop design integration

The importance of the manufacturing shop design in the successful operation of a production system is well documented and, as a result, significant work has been devoted to this area. Due to the inherent complexity of the global shop design problem, much research has focused on individual sub-problems, including the layout of manufacturing resources, the design of the shop aisle network, and the selection of the material handling equipment, rather than addressing the integrated problem directly. This paper reviews literature contributions to shop design integration, as well as partial integration schemes such as the design for operation and control of material handling systems. Furthermore, it examines the ability of models and solution approaches for individual sub-problems of shop design to support integration schemes.     

Human resource management and its impact on strategic business-IT alignment: A literature review and avenues for future research

From an information systems perspective, organizations striving to leverage a strategic alignment between Information Technology (IT) and business areas often underestimate the role of human resource management in creating business value. This literature review analyzes 71 scholarly articles to assess the role of human resource management in supporting the strategic alignment between business and IT. We identify the organizational role of individual human resources in strategic alignment, their contribution to more effective strategic alignment, and how human resource management supports such contribution. Based on these insights, we formulate propositions and identify avenues for future research.     

数据挖掘在医疗中的应用研究

随着医院信息化的进程加快,医院信息的积累越来越丰富,为医学研究提供了方便和研究价值.以出院病患者的随访记录建立数据库,依靠数据挖掘技术,筛选出有价值的信息,建立一个能够为以后的治疗提供有价值的参考作用的医院医疗方案库.从而形成一个对患者更负责任的体系,完善医院服务,提高医院诊断就医效率.     

Role of data dictionaries in information resource management

The role of information resource dictionary systems (data dictionary systems) is important in two important phases of information resource management:First, information requirements analysis and specification, which is a complex activity requiring data dictionary support: the end result is the specification of an “Enterprise Model,” which embodies the major activities, processes, information flows, organizational constraints, and concepts. This role is examined in detail after analyzing the existing approaches to requirements analysis and specification.Second, information modeling which uses the information in the Enterprise Model to construct a formal implementation independent database specification: several information models and support tools that may aid in transforming the initial requirements into the final logical database design are examined.The metadata — knowledge about both data and processes — contained in the data dictionary can be used to provide views of data for the specialized tools that make up the database design workbench. The role of data dictionary systems in the integration of tools is discussed.     

A voice-entry data management system: application in monoclonal antibody research

A computerized system for the management of hybridoma cell growth data using voice input is described. The system permits the storage, retrieval, and analysis of data for hybridoma cells secreting monoclonal antibodies. It consists of a local system residing on a professional computer interfaced to a mainframe processing system. The local system uses voice-input to facilitate data entry while data flagged for retention is stored on the mainframe. The system permits the investigator to monitor thousands of candidate cell lines until selection for specific antibody production is completed. All record-keeping aspects of hybridoma cell culture from establishment through freezing and storage of selected cell lines can be accomplished by personnel without computer expertise.     

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。