Data Mining In E-commerce Research Articles

A Critical Examination of Different Models for Customer Churn Prediction using Data Mining

Due to competition between online retailers, the need for providing improved customer service has grown rapidly. In addition to reduction in sales due to loss of customers, more investments are needed to be done to attract new customers. Companies now are working continuously to improve their perceived quality by way of giving timely and quality service to their customers. Customer churn has become one of the primary challenges that many firms are facing nowadays. Several churn prediction models and techniques are proposed previously in literature to predict customer churn in areas such as finance, telecom, banking etc. Researchers are also working on customer churn prediction in e-commerce using data mining and machine learning techniques. In this paper, a comprehensive review of various models to predict customer churn in e-commerce data mining and machine learning techniques has been presented. A critical review of recent research papers in the field of customer churn prediction in e-commerce using data mining has been done. Thereafter, important inferences and research gaps after studying the literature are presented. Finally, the research significance and concluding remarks are described in the end.

Research on e-commerce intelligent service based on Data Mining

With the rapid development of electronic commerce in China, a large amount of information data will be generated at every moment. How to excavate useful information is becoming an important problem In the big data age. Firstly, the smart service model of E-Commerce based on data mining was proposed, and user group mining, user interest mining, industry and domain knowledge mining and business association mining were used to bridge the gap between the big data application and requirements of smart service. Then the technical support system of E-Commerce data mining based on Hadoop platform was suggested to provide technical solution for implementation of smart service applications. And finally, the scenario knowledge recommendation service with the support of big data mining were discussed.

Data Mining in E-Commerce for Electronics Products

Data Mining in E-Commerce for Electronics Products

Data Mining in Electronic Commerce: Benefits and Challenges

Huge volume of structured and unstructured data which is called big data, nowadays, provides opportunities for companies especially those that use electronic commerce (e-commerce). The data is collected from customer’s internal processes, vendors, markets and business environment. This paper presents a data mining (DM) process for e-commerce including the three common algorithms: association, clustering and prediction. It also highlights some of the benefits of DM to e-commerce companies in terms of merchandise planning, sale forecasting, basket analysis, customer relationship management and market segmentation which can be achieved with the three data mining algorithms. The main aim of this paper is to review the application of data mining in e-commerce by focusing on structured and unstructured data collected thorough various resources and cloud computing services in order to justify the importance of data mining. Moreover, this study evaluates certain challenges of data mining like spider identification, data transformations and making data model comprehensible to business users. Other challenges which are supporting the slow changing dimensions of data, making the data transformation and model building accessible to business users are also evaluated. A clear guide to e-commerce companies sitting on huge volume of data to easily manipulate the data for business improvement which in return will place them highly competitive among their competitors is also provided in this paper.

Study of Data Mining Techniques in E-Commerce

E-commerce is kind of new business model,web data mining technology is an important new area of research applications in e-commerce.This article described the concepts and types of e-commerce and Web data mining technology and analyzed how to proceed Web data mining in e-commerce, elaborated Web data mining technology in e-commerce.

Data Mining in E-Commerce: A CRM Platform

Data is considered as a basic form of information that needs collection, management, mining and interpretation to create knowledge. Modern e-commerce is also vigorously developing that makes resources and services on the internet richly colorful. At the same time there are lots of fraudulent situations happening with people coming closer to the ecommerce system. This is an era where e-commerce is considered to be a killer-domain for successful mining data as it gives the apt ingredients from situation to situation. One of oldest things that e-commerce can do is customer relationship management (CRM). Businesses targeting customers has a direct link with the economy of a country as the current ecommerce system is used by people from lay man to business tycoons. The paper aims at a study on e-commerce with data mining proposing architectural model integrating an improved CRM system for handling business predictions and make strategies to enhance best customer relationship management.

Privacy Preserving Combinatorial Function for Multi-Partitioned Data Sets

preserve the electronic fitness records of data mining applications, Data perturbation is used. It is a form of privacy- preserving data mining. An improved amount and range of information stored in databases has direct to an enhancement in the desire for ranked and best match queries. Such queries are mainly applicable when dealing with privacy- sensitive information. To facilitate privacy preservation in data mining or machine learning algorithms over horizontally partitioned or vertically partitioned data, many protocols have been proposed using SMC and various secure building blocks. In this work, we plan to present an effective and efficient cluster based privacy preserving data perturbation technique to mine Multi-partitioned data sets that comprises of both vertical and horizontal data sets which is current demand of e- business and e-commerce data mining environment. To evaluate a trade of between data privacy and transparency of individual's data, data perturbation technique is presented with validation and authentication. In multi-partitioned data distribution, data perturbation raised ambiguity between vertical and horizontal partitions of the data. To overcome the ambiguity, we plan to introduce divisive k-neighbor clusters for multi-partitioned data sets. The performance is evaluated with bench data sets obtained from popular e-business / e- commerce sites. (Amazon, e-bay etc.,). Keywords-partitioned data sets, Privacy preservation, Data perturbation technique.

A problem of data mining in E-commerce

Data mining is generally defined as the science of nontrivial extraction of implicit, previously unknown, and potentially useful information from datasets. There are many websites on the Internet that provide extensive information about products and allow users post comments on various products and rate the product on a scale of 1 to 5. During the past decade, the need for intelligent algorithms for calculating and organizing extremely large sets of data has grown exponentially. In this article we investigate the extent to which a product’s average user rating can be predicted, using a manageable subset of a data set. For this we use a linearization-algorithm based prediction model and sketch how an inverse problem can be formulated to yield a smooth local volatility function of user ratings. The MAPLE programs that implement the proposed algorithm show that the method is reasonably accurate for the reconstruction of volatility of user ratings, which is useful both in accurate user predictions as well as computing sensitivity.

Data mining in e-commerce: A survey

Data mining has matured as a field of basic and applied research in computer science in general and e-commerce in particular. In this paper, we survey some of the recent approaches and architectures where data mining has been applied in the fields of e-commerce and e-business. Our intent is not to survey the plethora of algorithms in data mining; instead, our current focus being e-commerce, we limit our discussion to data mining in the context of e-commerce. We also mention a few directions for further work in this domain, based on the survey.