Data Mining in Electronic Commerce: Benefits and Challenges  

作　　者：Mustapha Ismail Mohammed Mansur Ibrahim Zayyan Mahmoud Sanusi Muesser Nat 

机构地区：[1]Management Information Systems Department, Cyprus International University, Haspolat, Lefkosa via Mersin, Turkey

出　　处：《International Journal of Communications, Network and System Sciences》2015年第12期501-509,共9页通讯、网络与系统学国际期刊（英文）

摘　　要：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.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.

关 键 词：DATA Mining BIG DATA E-COMMERCE CLOUD Computing 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]