基于关联分类方法的网站客户分类研究

随着电子商务的不断发展,对用户的分析和分类越来越重要,因此需要一个行之有效的方法来对用户进行分类。针对网站日志数据的特点和各种数据挖掘算法的应用特征,尝试用基于关联规则的分类算法来对网站客户进行分类。实验证明此方法是有效的,其结果可以作为提供个性化服务的依据。     

Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal

This work is focused on the usage analysis of a citizen web portal, Infoville XXI (http://www.infoville.es) by means of Self-Organizing Maps (SOM). In this paper, a variant of the classical SOM has been used, the so-called Growing Hierarchical SOM (GHSOM). The GHSOM is able to find an optimal architecture of the SOM in a few iterations. There are also other variants which allow to find an optimal architecture, but they tend to need a long time for training, especially in the case of complex data sets. Another relevant contribution of the paper is the new visualization of the patterns in the hierarchical structure. Results show that GHSOM is a powerful and versatile tool to extract relevant and straightforward knowledge from the vast amount of information involved in a real citizen web portal.     

Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques

Enterprise Resource Planning systems tend to deploy Supply Chain Management and/or Customer Relationship Management techniques, in order to successfully fuse information to customers, suppliers, manufacturers and warehouses, and therefore minimize system-wide costs while satisfying service level requirements. Although efficient, these systems are neither versatile nor adaptive, since newly discovered customer trends cannot be easily integrated with existing knowledge. Advancing on the way the above mentioned techniques apply on ERP systems, we have developed a multi-agent system that introduces adaptive intelligence as a powerful add-on for ERP software customization. The system can be thought of as a recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system can be periodically retrained as new information is added to the ERP. In this paper, we present the architecture and development details of the system, and demonstrate its application on a real test case.     

Biclustering in data mining

Biclustering consists in simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets (classes). Samples and features classified together are supposed to have a high relevance to each other. In this paper we review the most widely used and successful biclustering techniques and their related applications. This survey is written from a theoretical viewpoint emphasizing mathematical concepts that can be met in existing biclustering techniques.     

基于K-means算法的税务客户分类研究与应用

随着税务信息化技术的快速发展,将数据挖掘技术引入税务管理系统已经成为关注重点。以税务客户的信息为研究对象,运用K-means聚类算法对税务客户合理分析,划分客户群组,按不同的方法处理不同的客户群,提供有针对性、个性化的管理和服务。     

混合模式的网络流量分类方法

为了更好地满足用户对各类Internet业务服务质量越来越精细的要求,流量分类是网络管理的重要环节之一。通过分析、对比基于端口号匹配、特征字段分析和流统计特征的机器学习分类方法的应用现状及其优缺点,针对单一分类方法存在的分类准确度不高、分类时间长等问题,提出一种混合模式的网络流量分类方案。此方案结合端口号匹配和机器学习分类方法,采用输出结果可视化的自组织映射网络算法实现网络流量在应用层的分类。实验表明,该方案能有效地实现对网络流量应用类型的分类,分类结果可视化效果好。     

基于聚类的数据挖掘技术在电子商务CRM中的应用研究

本文通过对电子商务中客户关系管理聚类数据挖掘技术的研究,提出基于CABOSFV算法的客户聚类算法,用于解决客户关系管理中大量高维稀疏数据组成的客户行为数据集聚类分析和信息管理问题。     

基于动态聚类的证券业客户细分实证研究

在客户关系管理理论基础上,建立了一个包含13个行业特色指标的证券业客户多维细分模型,并利用聚类分析对国内某知名券商的具体客户信息和交易数据进行了实证研究,有效识别出了具有不同特征以及偏好的客户群,并在此基础上提出了相应的营销策略。     

一个文本挖掘方法在扩展的电子商务系统中的应用

数据挖掘技术是电子商务系统不可缺少的重要工具，两者的结合具有长远的发展前途。初探了将一种基于SOM的文本聚类方法应用于一个扩展的电子商务系统的方法，使之用来进行注册客户的聚类挖掘，有利于充分利用网络资源，提高网络系统的使用效率。     

Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom company’s case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance.     

应用于CRM的一类马氏链模型求解及其分析

给出了用于研究客户关系管理（Customer Relationship Management,CRM）模型中的一类马氏链数学模型（Pfeifer模型）的收益期望值的解析解（无限次交易条件下）,以方便该类模型的研究和分析。借助于求逆公式,将V=（I-P）^-1R方程中矩阵求逆部分进行分解和简化,解出矩阵逆的解析解,从而求解出该类模型收益期望值向量的解析解,并推广到n阶。基于该解析解,对该类模型收益总期望值的特性进行了简单分析和讨论,该收益期望值的解析解将给该类方程的解析分析提供帮助。     

Mixing numerical and categorical data in a Self-Organizing Map by means of frequency neurons

Even though Self-Organizing Maps (SOMs) constitute a powerful and essential tool for pattern recognition and data mining, the common SOM algorithm is not apt for processing categorical data, which is present in many real datasets. It is for this reason that the categorical values are commonly converted into a binary code, a solution that unfortunately distorts the network training and the posterior analysis. The present work proposes a SOM architecture that directly processes the categorical values, without the need of any previous transformation. This architecture is also capable of properly mixing numerical and categorical data, in such a manner that all the features adopt the same weight. The proposed implementation is scalable and the corresponding learning algorithm is described in detail. Finally, we demonstrate the effectiveness of the presented algorithm by applying it to several well-known datasets.     

A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization

Customer clustering is an essential step to reduce the complexity of large-scale logistics network optimization. By properly grouping those customers with similar characteristics, logistics operators are able to reduce operational costs and improve customer satisfaction levels. However, due to the heterogeneity and high-dimension of customers’ characteristics, the customer clustering problem has not been widely studied. This paper presents a fuzzy-based customer clustering algorithm with a hierarchical analysis structure to address this issue. Customers’ characteristics are represented using linguistic variables under major and minor criteria, and then, fuzzy integration method is used to map the sub-criteria into the higher hierarchical criteria based on the trapezoidal fuzzy numbers. A fuzzy clustering algorithm based on Axiomatic Fuzzy Set is developed to group the customers into multiple clusters. The clustering validity index is designed to evaluate the effectiveness of the proposed algorithm and find the optimal clustering solution. Results from a case study in Anshun, China reveal that the proposed approach outperforms the other three prevailing algorithms to resolve the customer clustering problem. The proposed approach also demonstrates its capability of capturing the similarity and distinguishing the difference among customers. The tentative clustered regions, determined by five decision makers in Anshun City, are used to evaluate the effectiveness of the proposed approach. The validation results indicate that the clustered results from the proposed method match the actual clustered regions from the real world well. The proposed algorithm can be readily implemented in practice to help the logistics operators reduce operational costs and improve customer satisfaction levels. In addition, the proposed algorithm is potential to apply in other research domains.     

SaaS模式的中小企业客户关系管理研究

客户关系管理是提高企业核心竞争力的关键之一,传统客户关系管理模式无法满足广大中小企业的需求。在讨论新的SaaS概念及SaaS模式与传统模式比较的基础上,分析了基于SaaS模式的中小企业客户关系管理优势,提出了基于SaaS模式的中小企业客户关系管理解决方案,构建了SaaS模式解决方案的逻辑体系结构,并指出了中小企业实施SaaS模式客户关系管理应注意的问题。     

Selection criteria for text mining approaches

Text mining techniques include categorization of text, summarization, topic detection, concept extraction, search and retrieval, document clustering, etc. Each of these techniques can be used in finding some non-trivial information from a collection of documents. Text mining can also be employed to detect a document’s main topic/theme which is useful in creating taxonomy from the document collection. Areas of applications for text mining include publishing, media, telecommunications, marketing, research, healthcare, medicine, etc. Text mining has also been applied on many applications on the World Wide Web for developing recommendation systems. We propose here a set of criteria to evaluate the effectiveness of text mining techniques in an attempt to facilitate the selection of appropriate technique.     

Adjusting Fuzzy Similarity Functions for use with standard data mining tools

Data mining is crucial in many areas and there are ongoing efforts to improve its effectiveness in both the scientific and the business world. There is an obvious need to improve the outcomes of mining techniques such as clustering and other classifiers without abandoning the standard mining tools that are popular with researchers and practitioners alike. Currently, however, standard tools do not have the flexibility to control similarity relations between attribute values, a critical feature in improving mining-clustering results. The study presented here introduces the Similarity Adjustment Model (SAM) where adjusted Fuzzy Similarity Functions (FSF) control similarity relations between attribute values and hence ameliorate clustering results obtained with standard data mining tools such as SPSS and SAS. The SAM draws on principles of binary database representation models and employs FSF adjusted via an iterative learning process that yields improved segmentation regardless of the choice of mining-clustering algorithm. The SAM model is illustrated and evaluated on three common datasets with the standard SPSS package. The datasets were run with several clustering algorithms. Comparison of “Naïve” runs (which used original data) and “Fuzzy” runs (which used SAM) shows that the SAM improves segmentation in all cases.     

On data mining,compression, and Kolmogorov complexity

Will we ever have a theory of data mining analogous to the relational algebra in databases? Why do we have so many clearly different clustering algorithms? Could data mining be automated? We show that the answer to all these questions is negative, because data mining is closely related to compression and Kolmogorov complexity; and the latter is undecidable. Therefore, data mining will always be an art, where our goal will be to find better models (patterns) that fit our datasets as best as possible.     

Enhanced customer requirement classification for product design using big data and improved Kano model

The classification of customer requirements (CRs) has a significant impact on the solution of product design. Existing CRs classification methods such as the Kano model and IPA model are time-consuming and inaccurate. This paper proposes a CRs classification method for product design using big data of online customer reviews of products to classify CRs accurately and efficiently. Comments of customer reviews are matched to CRs using a hierarchical semantic similarity method. Customer satisfaction degrees are defined based on emotional levels of adjectives and adverbs of customer comments using word vectors. The function implementation degree of each product is determined by specifications crawled from online products. Fitting curves are formed by defined customer satisfaction and function implementation of CRs using polynomial modeling and least square methods. Based on the slope of the fitted curves, CRs are classified to provide the minimum and maximum function implementations of CRs in each CR group to guide a product design process. The proposed method is applied in a case study of defining CRs classifications for design of upper limb rehabilitation devices. For verifying the proposed method, CRs defined by the existing methods are compared with CRs from the proposed method in design of an upper limb rehabilitation device.     

The priority factor model for customer relationship management system success

As the market competition becomes keen, constructing a customer relationship management system is coming to the front for winning over new customers, developing service and products for customer satisfaction and retaining existing customers. However, decisions for CRM implementation have been hampered by inconsistency between information technology and marketing strategies, and the lack of conceptual bases necessary to develop the success measures. Using a structural equation analysis, this study explores the CRM system success model that consists of CRM initiatives: process fit, customer information quality, and system support; intrinsic success: efficiency and customer satisfaction; and extrinsic success: profitability. These constructs underlie much of the existing literature on information system success and customer satisfaction perspectives. We found the empirical support for CRM implementation decision-making from 253 respondents of 14 companies which have implemented the CRM system. These findings should be of great interest to both researchers and practitioners.     

KNN-kernel density-based clustering for high-dimensional multivariate data

Density-based clustering algorithms for multivariate data often have difficulties with high-dimensional data and clusters of very different densities. A new density-based clustering algorithm, called KNNCLUST, is presented in this paper that is able to tackle these situations. It is based on the combination of nonparametric k-nearest-neighbor (KNN) and kernel (KNN-kernel) density estimation. The KNN-kernel density estimation technique makes it possible to model clusters of different densities in high-dimensional data sets. Moreover, the number of clusters is identified automatically by the algorithm. KNNCLUST is tested using simulated data and applied to a multispectral compact airborne spectrographic imager (CASI)_image of a floodplain in the Netherlands to illustrate the characteristics of the method.