客户分类

提出了一种基于动态SOM神经网络和RFM指标的客户分类方法。

A customer segmentation method based on dynamic SOM and RFM indicators is presented .

第三，从客户分类、客户关系管理、客户关系管理系统（CRM）的建立，提出了实质性的指导。

Thirdly , from customer segmentation , customer relationship relationship management , customer relationship management ( CRM ) systems , the establishment of proposed substantive guidance .

本文讨论在一个具体的电子商务网站环境下，Web挖掘模式对客户分类的支持作用。

The article discusses customer-sort support of web mining model in a specific E-commerce website .

客户分类是智能CRM系统的一个重要功能。

The customer classifier is an important part of an Intelligent CRM system .

将多分类器融合技术用于CRM中的客户分类研究，以提高分类性能。

Applying multi-classifiers fusion technology to customer classification in CRM to improve the classification performance .

基于CRM与数据挖掘的客户分类研究

Customers Classification Based on CRM and DM

CRM中的客户分类管理

Customer - Classified Management in CRM

自组织映射神经网络（SOM）在客户分类中的一种应用

An Application of SOM Neural Network in Customer Classification

用于客户分类的K-means算法

K-means Algorithm for the Classification of Customers

探讨了在CRM系统中，基于模糊ISODATA的客户分类模型。

The paper discussed a model of customer classification based on Fuzzy ISODATA in CRM system .

应用C4.5算法构造客户分类决策树的方法

Method of Building Decision Trees of Customer Classification by Using C4.5 Algorithm

客户分类管理是CRM的核心内容之一，科学有效的客户分类管理对于一个企业具有重要的意义。

Customer-classified management is one of the core content of CRM . It is of important meaning for an enter-prise to classify customer validly and efficiently .

基于RFM和粗糙集的客户分类规则提取

An extraction of customer classification rule based on RFM and rough set

该模型选取RFM（近度，Recency；频度，Frequency；值度，Monetary）作为客户分类的特征指标，采用模糊聚类最大矩阵元法确定分类数。

This model was put into practice by adopting the featuring parameters of RFM ( Recency , Frequency , Monetary ) and using the method of maximal matrix element to ascertain the number of classification .

最后，本文对我们所提供的解决方案的实现环境做了简单介绍，并给出了一个基于数据仓库和OLAP进行数据挖掘的实例，即客户分类分析。

At the last part of the article , it introduces the realization environment in brief , and gives an example of customer classification analysis in which implements data mining based on data warehouse and OLAP .

客户分类是分析型CRM的重要组成部分，建立在客户分类基础上的一对一营销更是企业提高客户的满意度，增加获利能力的必要手段。

The customer classification is the important component of analytic type CRM . The " marketing one to one " that sets up on customer classification is that enterprises improve the customer 's satisfaction and increase the earning capacity essential means .

数据挖掘（决策树算法）在客户分类中的应用

Application of Data Mining ( decision-making calculation ) in Customer Segmentation

基于模糊积分的客户分类不确定性优化研究

Study on optimization of uncertainty of client classification based on fuzzy integral

基于决策树和相异度算法的移动通信客户分类方法

Classification of mobile communication customers based on decision tree and dissimilarity algorithm

基于相似粗糙集的客户分类管理研究

Study of Customer-Classified Management Based on Similar Rough Sets

准确的客户分类是企业有效地实施客户关系管理的基础。

Exact customer segmentation is the base of effectively implementation customer relationship management .

基于模糊聚类的客户分类方法研究

Research on Customer Classification Method Based on Fuzzy Clustering

基于市场营销离群数据挖掘的客户分类模型

Client Sorting Model Based on Marketing Outlier Mining

决策树算法在客户分类中的应用

The Application of Decision Tree in Customer Classification

动静态结合的图像编码动静态属性数据相结合的客户分类方法研究

Research on Coding of Compound Images Customer Segmentation Based on Integration of Dynamic and Static Attributes

基于区间数信息的客户分类方法&客户资产价值体系的构建与应用

Customer Classification Based on Interval Number Information & Customer Asset Value System 's Construction and Application

文章还利用客户分类管理矩阵对客户进行了分类管理。

The customer classification management is also analyzed through using for the matrix of customer classification management .

运用客户分类方法对大连移动通信公司的客户进行了分类，并在此基础上提出了适合的财务策略。

And the thesis brings forward the available financial strategy on the basis of the customer profitability .

测试结果表明，该组合分类算法在移动通信客户分类时的平均准确率达到了83.1%。

Experimental results show that the average classification accuracy of mobile communication customers is as high as83.1 % .

任何经手现金的人员，是否很难获得客户分类帐和月结单资料？

Is it difficult for any person handling cash to obtain access to customers ' ledgers and monthly statements ?