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针对数据挖掘方法在电信客户流失预测中的局限性,提出将信息融合与数据挖掘相结合,分别从数据层、特征层、决策层构建客户流失预测模型。确定客户流失预测指标;根据客户样本在特征空间分布的差异性对客户进行划分,得到不同特征的客户群;不同客户群采用不同算法构建客户流失预测模型,再通过人工蚁群算法求得模型融合权重,将各模型的预测结果加权得到预测最终结果。实验结果表明,基于信息融合的客户流失预测模型确实比传统模型更优。 相似文献
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刘城霞 《计算机技术与发展》2011,21(6)
Microsoft神经网络算法是基于人体神经网络系统模拟而成的一种算法,它对于数据挖掘的发展有着很大的推动性.为了进一步发展基于神经网络算法的数据挖掘系统的应用,在Microsoft神经网络算法的基础上构建了一个数据挖掘商业应用实例系统,通过研究客户的一些个人属性以及办理业务的基本情况,预测客户的信誉情况、业务的办理趋向、银行开展新业务的趋向等.在实例系统的构建过程中,对神经网络数据挖掘算法的挖掘过程进行了详细的分析,促进了数据挖掘的应用实践. 相似文献
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阐述了饰品企业营销的现状,提出了将数据挖掘技术应用到饰品营销中的方案.在分析决策树算法的基础上,介绍了决策树算法及决策树的构造,并使用该算法对企业客户进行分类及对新客户类型预测,实现对商业数据中隐藏信息的挖掘,且对该挖掘模型进行了验证. 相似文献
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概述数据挖掘,阐明数据挖掘的概念、数据挖掘在商业中的意义.结合大型商场管理系统提出了一种基于遗传算法的最优客户群体数据挖掘算法,以实例说明应用遗传算法中需要注意的一些问题. 相似文献
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客户流失分析是一种具有预测性的数据挖掘分析技术。文章针对C R M客户关系管理中的客户流失问题,从数据挖掘技术几个经典算法出发,探讨企业管理层和信息技术层在CRM客户关系管理中对客户流失问题的关注。 相似文献
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《电子制作.电脑维护与应用》2021,(2)
近年来,机器学习和数据挖掘成为大数据领域的一个重要研究热点。Spark并行处理框架是一个当今高速发展应用广泛的生态系统,是专为大规模数据处理而设计的快速通用的计算引擎。本文尝试使用逻辑回归算法,使用Spark对银行营销数据进行建模分析,根据得到的模型预测客户是否订阅存款业务。 相似文献
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CRM中数据挖掘技术应用概述 总被引:6,自引:0,他引:6
主要介绍了数据挖掘技术在CRM中的应用,具体讨论了数据挖掘技术在客户生命周期阶段的应用和在CRM上创建数据挖掘技术的过程,最后介绍了一些数据挖掘算法在CRM上的应用。 相似文献
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Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decisions can be improved. Various types of association rules may exist in a large database of customer transactions. The strategy of mining association rules focuses on discovering large item sets, which are groups of items which appear together in a sufficient number of transactions. We propose a graph-based approach to generate various types of association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large item sets. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database 相似文献
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Ayhan Demiriz 《Data mining and knowledge discovery》2004,9(2):147-170
Commercial recommender systems use various data mining techniques to make appropriate recommendations to users during online, real-time sessions. Published algorithms focus more on the discrete user ratings instead of binary results, which hampers their predictive capabilities when usage data is sparse. The system proposed in this paper, e-VZpro, is an association mining-based recommender tool designed to overcome these problems through a two-phase approach. In the first phase, batches of customer historical data are analyzed through association mining in order to determine the association rules for the second phase. During the second phase, a scoring algorithm is used to rank the recommendations online for the customer. The second phase differs from the traditional approach and an empirical comparison between the methods used in e-VZpro and other collaborative filtering methods including dependency networks, item-based, and association mining is provided in this paper. This comparison evaluates the algorithms used in each of the above methods using two internal customer datasets and a benchmark dataset. The results of this comparison clearly show that e-VZpro performs well compared to dependency networks and association mining. In general, item-based algorithms with cosine similarity measures have the best performance. 相似文献
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提出了一种基于关联规则挖掘的聚类方法。首先,通讯行业客户行为的原始数据经过数据预处理转变为地区间的“距离”数据。其次,由于地区是“漂浮”的,不再是“刚体”,而是一种抽象的“柔性”距离,使用关联规则进行挖掘成为一种好的选择。文中对通讯行业客户行为进行了基于关联规则的建模,较好地嵌入了关联规则的框架。在数据实验后,提炼出了知识,发现东南亚客户聚成一类,以此为模式,得出了“在南美发展业务是错误的”的结论,该结论在挖掘之前是没有意料到的。实践上,该结论阻止了相应公司的南美发展计划,为公司度过后来的硅谷经济萧条时期省下了上百万美元的“战略储备”资金。 相似文献
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Lihong Jiang Jianyi Wang Nazaraf Shah Hongming Cai Chengxi Huang Ray Farmer 《Service Oriented Computing and Applications》2016,10(3):303-315
Business process models which are usually constructed by business designers from experience and analysis are the main guidelines for services composition in the service-oriented architecture (SOA) applications development. However, due to the complexity of business models, it is a challenging task for business process designers to optimize the process models dynamically in accordance with changes in business environments. In this paper, a process-mining-based method is proposed to support business process designers to monitor efficiency or capture the changes of a business process. Firstly, we define a scenario model to depict business elements and their relationships which are critical to business process design. Based on the proposed scenario model, process mining algorithms, including control flow mining, roles mining and data flow mining are carried out in a certain sequence synthetically to extract business scenarios from event logs recorded by SOA application systems. Finally, we implement a prototype using a logistic scenario to illustrate the feasibility of our method in SOA applications development. 相似文献
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王平水 《计算机工程与应用》2010,46(30):115-116
关联规则挖掘是数据挖掘的一个重要研究领域。针对经典Apriori算法频繁扫描事务数据库致使运行效率低下的缺点,在研究已有关联规则挖掘算法的基础上,提出一种改进的基于关系矩阵的关联规则挖掘算法。理论分析和实验结果均表明,所提算法是高效的和实用的。 相似文献
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数据挖掘技术在证券客户关系中的应用 总被引:2,自引:2,他引:0
研究证券管理问题,客户关系管理系统(CRM)是现代经营管理科学与现代信息技术结合的科学问题.数据挖掘技术是有效地利用现有数据资源的重要手段.重点是针对数据挖掘技术在证券客户关系管理中的具体问题.运用数据仓库技术建立了客户交易行为数据仓库,并运用聚类技术完成了基于证券公司客户交易行为数据仓库的证券公司客户细分.基于数据挖掘的CRM是对传统企业管理思想的一个创新,充分体现了管理的科学性和艺术性.对企业的经营决策和客户关系管理都具有相当重要的作用和意义. 相似文献
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Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application. 相似文献
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隐私保护的数据挖掘近年来已经为数据挖掘的研究热点,Web网站的服务器日志保存了用户访问页面的信息,如果不加以保护会导致用户隐私数据的泄漏。针对这个问题,讨论了在Web数据挖掘中用户行为的隐私保护问题,进而提出一种将Web服务器日志信息转换成关系数据表的方法,并通过随机化回答方法产生干扰数据表项中信息,再以此为基础,提供给数据使用者进行频繁项集以及强关联规则的发现算法,从而得到真实保密的网上购物篮商品间的关联规则。经实验证明,提出的Web使用挖掘中的隐私保护关联规则挖掘算法隐私性较好,具有一定的适用性。 相似文献
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Qiang Yang Jie Yin Ling C. Rong Pan 《Knowledge and Data Engineering, IEEE Transactions on》2007,19(1):43-56
Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the discovered knowledge manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase in the objective function such as profit. In this paper, we present novel algorithms that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing an objective function: the expected net profit. These algorithms can discover cost-effective actions to transform customers from undesirable classes to desirable ones. The approach we take integrates data mining and decision making tightly by formulating the decision making problems directly on top of the data mining results in a postprocessing step. To improve the effectiveness of the approach, we also present an ensemble of decision trees which is shown to be more robust when the training data changes. Empirical tests are conducted on both a realistic insurance application domain and UCI benchmark data 相似文献