sis 18(16): e5

Research Article

Features Analysis of Online Shopping System Using WCM

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  • @ARTICLE{10.4108/eai.13-4-2018.154471,
        author={Maria  Erum and Muhammad  Waqas and Sidra  Arshad and Tahir  Nawaz},
        title={Features Analysis of Online Shopping System Using WCM},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        keywords={WCM (Web Content Mining), Online Shopping, Classification, Data Mining Techniques, Decision Tree, Na\~{n}ve Bayes, J48.},
  • Maria Erum
    Muhammad Waqas
    Sidra Arshad
    Tahir Nawaz
    Year: 2018
    Features Analysis of Online Shopping System Using WCM
    DOI: 10.4108/eai.13-4-2018.154471
Maria Erum, Muhammad Waqas1, Sidra Arshad1, Tahir Nawaz1,*
  • 1: Department of Computer Science, The University of Lahore, (Sargodha Campus)
*Contact email:


Data mining techniques being used for web information extraction are unbelievable systems and suggested for the protection of extremely susceptible data. By the web sources huge amount of data is maintained and can be easily retrieved by using the web mining techniques as the techniques are applied exactly based on the needs of the users. ECommerce and online shopping noticed a huge growth in business industry. This facility has been mostly employed in western countries during the last two decades. In east online shopping is increasing as most of the business is running through web site as well as in west. This business can be boost by feature analysis of different successful running online web stores. This study is going to present analysis of different features of successful online business website and of those which are not that much popular and accessed infrequently, their features will be extracted and compared to get the reasons of popularity of frequently accessed online shopping websites and after that recommendations will be made to increase the traffic of unpopular online shopping websites to dominate online business in Pakistan. According to the presented work it has been concluded that unpopular websites lack some features that are included in popular websites such as brands, as people are more conscious about brands and labels so they visit and shop from the websites which offer them best quality famous brands, moreover it has been observed that unpopular websites have less categories they must broaden their variety of products especially related to sports, fitness, bathroom accessories, technology and cosmetics. Apart from these another interesting fact that has been found is that popular websites mostly attract their customers by giving them offers such as buy one get one free, free home delivery and free gifts, such offers are always attracting new and more customers. Unpopular websites can improve their business by including the features discussed above. The results of research on this dataset also show that the Naïve bayes is better than j48 in terms of efficiency and accuracy respectively.