面向在线顾客点评的属性依赖情感知识学习

该文研究属性依赖情感知识学习。首先提出了一个新颖的话题模型,属性观点联合模型(Joint Aspect/Opinion model, JAO),来同时抽取评论实体属性及属性相关观点词信息。在此基础上,对于各个属性,构造属性依赖的词关系图,并在该图上应用马尔科夫随机行走过程来计算观点词到少量褒、贬种子词的游走时间(Hitting Time),进而估计这些词的属性依赖的情感极性分值。在餐馆点评数据上的实验表明所提出的方法能有效抽取属性相关观点词,同时有效估计其属性依赖的情感极性分值。     

基于组合预测的电信客户流失预测

利用数据挖掘技术,对电信行业客户信息进行了分析,对用户进行关联、分类、聚类分析,给出了解决问题的模型和方法并在实践中得到评估和检验,本文提出了一种基于组合预测理论的预测算法,在实际客户流失预测分析的应用结果表明,该算法的流失预测的准确率高于传统的分类预测算法所得到的预测准确率.构建的预测模型对解决电信客户流失预测方面的问题具有应用价值.     

超级计算中心客户细分及差异化服务策略研究

本文以中国科学院计算机网络信息中心超级计算中心（以下简称超级计算中心）客户服务工作为研究对象,运用 K-means 算法对客户进行细分,进而对每类客户群提出相应的差异化服务策略。实施差异化服务策略可以更好地分配资源、提供更有效的客户服务。     

WLAN广域覆盖技术及应用策略研究

目前,WLAN的主要建设模式以室内独立放装和室分系统合路方式为主,室外广域覆盖模式应用策略不明确,规划建设不是很成熟。对WLAN广域覆盖存在的问题和解决思路进行了研究,并进行了大量的测试工作,对测试结果进行了深入分析,最终给出了WLAN广域覆盖的应用建议。     

SQL Server 2005数据挖掘技术在证券客户忠诚度的应用

文中主要研究了我国证券业客户忠诚度分类和表现形式,提出了一种证券业客户忠诚度评估的有效方法。依据RFM客户评价方法,结合数据挖掘的一般流程将SOLServer2005中的数据挖掘技术应用于证券业客户忠诚度模型系统中,并结合某证券公司客户交易数据,对其客户忠诚度进行了准确合理的分类,对其不同忠诚度类型的客户提出相应个性化营销建议,最后通过使用DMX语言在客户端运用数据挖掘产生的分类规则对其客户进行了准确预测。     

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

聚类算法在银行客户细分中的应用

针对聚类算法在金融领域广泛应用的实际情况,基于银行客户数据集,对DBSCAN, K-means和X-means 3种聚类算法在执行效率、可扩展性、异常点检测能力等方面进行对比分析,并提出将X-means算法应用于银行业客户细分。利用X-means算法建立了一套银行客户细分模型,为银行决策者提供科学的决策支持。     

医院客户关系管理系统体系结构设计

HCRM是医院客户关系管理系统的简称。其主要目的是通过建立HCRM系统,想方设法为客户提供全方位的、满意的服务,以维持和保留现有客户,吸引潜在客户,去提高医院的核心竞争力。该文根据客户关系管理的管理理念和机制,结合医院管理的实际情况,对医院客户关系管理(HCRM)软件的需求分析、数据字典的准备等进行深入细致的研究。将统一建模语言(UML)应用于系统的开发设计中,采用先进的开发工具,数据交换为基于网络的C/S结构,自顶向下设计,考虑用户的特定需求,采用面向对象的方法设计用户界面,并提出相应的解决方案。     

基于代价敏感的决策树的电信离网分析模型

随着电信行业竞争的加剧,客户流失率日益攀升,因此提高客户流失的预测精度将直接关系到电信企业的生存和发展.而电信客户数据集中存在严重的数据不平衡问题,会导致两类错分代价明显不等同.而基于传统决策树的客户流失模型却是在两类错分代价相等的前提下建立的,与实际情况不符.因此引入代价敏感学习理论,该理论将不同的错分代价纳入建模过程,以建立一个基于代价敏感的决策树的电信客户离网分析模型.该方法有效地提高了模型对流失客户的预测性能.这对促进电信业的发展具有相当重要的意义.     

基于CRM的订单管理系统

从客户关系管理的角度,对企业的订单管理进行了研究。分析了以客户为中心的订单管理的特点及其订单处理流程,提出了一个以客户为中心的订单管理解决方案。