(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 1, 2019 352 | Page www.ijacsa.thesai.org A Qualitative Analysis to Evaluate Key Characteristics of Web Mining based e-Commerce Applications Sohail Tariq 1 , Ramzan Talib 2 , Muhammad Kashif Hanif 3 , Muhammad Umar Sarwar 4 , Hafiz Muhammad Rashid 5 , Muhammad Zaman Khalid 6 Department of Computer Science Government College University, Faisalabad Pakistan Abstract—E-Commerce applications are playing vital role by providing competitive advantage over business peers. It is important to get interesting patterns from e-commerce transactions to analyze customer experience, customer likelihood. For this, web mining based e-commerce applications are being developed for various e-businesses. There are different characteristics like user interface and interactivity, which can make these applications more efficient and effective. Well- defined criteria are needed to prioritize key characteristics of these applications. The primary intention of this work is to identify and prioritize the key characteristics and their impact on designing these applications. This paper provides a qualitative survey based evaluation and prioritization of key characteristics. Keywords—Web mining; e-Commerce applications; user interface; interactivity I. INTRODUCTION Due to the growing popularity and accessibility of internet, web has emerged as a popular source for information distribution, retrieval and analysis in recent years. Web is the data repository containing huge volume, variety and velocity of data [1]. Users are facing different problems for searching required information from different e-commerce websites. There should be an efficient mechanism to provide desired information to the user. For this purpose, web mining can be used for the extraction useful information for the users using different tools and technologies. Fig. 1 shows web mining classification [2]. Web mining can be classified into four categories based on content, structure, usage, and user profile. E-commerce applications can take advantage of data and web mining for improving the user experience. Web mining and data mining is often used to provide products and user interface according to user preferences. These applications are usually known as web mining based e-commerce application. Many researchers have already identified different characteristics for efficient and practical designing of web mining based e-commerce application [3, 4]. Some of the characteristics (what are different characteristics). However, nobody has explicitly researched the key characteristics of web mining based e-commerce applications. The focus of this study is to identify, evaluate, and prioritize the basic characteristics for web mining based e- commerce applications. In this research we will address following hypothesis: H o : Age is associated with user interface H 1 : Age is associated with navigation H 2 : Age is associated with data placement H 3 : Age is associated with convenience H 4 : Age is associated with interactivity H 5 : Gender is associated with user interface H 6 : Gender is associated with navigation H 7 : Gender is associated with data placement H 8 : Gender is associated with convenience H 9 : Gender is associated with interactivity The rest of the paper is organized in different sections. Section 2 presents the related work. Section 3 discusses proposed methodology. Results and discussions are described in Section 4. We conclude the outcomes with future work in Section 5. Fig. 1. Web Mining Classification. II. LITERATURE REVIEW A lot of work and analysis is done on World Wide Web. Web is a collection of inter related web pages and files that are stored on web servers. A large amount of data is stored on those servers that can help in growing a business. Web mining helps business owners to take new decisions for the growth of their business. The task can only be possible by using web mining applications in the context of E-commerce [5]. The key idea in the web mining tools is based on the statistical analysis, knowledge discovery and prediction model. Firstly, work start with statistical analysis, in which data analyzed by using