(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 2, 2019 Browsing Behaviour Analysis using Data Mining Farhana Seemi 1 , Hania Aslam 2 , Hamid Mukhtar 3 , Sana Khattak 4 1,2 National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan 3 College of CIT, Taif University, Taif, Saudi Arabia 4 University of Engineering and Technology (UET), Peshawar, Peshawar, 25000, Pakistan Abstract—Now-a-days most of our time is spent online using some form of digital technology such as search engines, news portals, or social media websites. Our online presence makes us engaged most of the time and leads us to become oblivious of our important work, resulting in a form of procrastination that decreases our productivity significantly. Some desktop and mobile applications have recently emerged to counter the problem by introducing various means of self-tracking to reduce the wasting of time and engage in productive activities. However, these systems suffer several shortcomings in terms of being static or providing a limited view of actions using one aspect only. To promote self-awareness that helps bring positive changes in individual’s performance, there is a need to present the data in a more persuasive ways, bringing interaction to it and present the same data in different ways using both temporal and cate- gorical dimensions. We describe a framework that collects and processes the browsing data and creates a user behavior model to extract valuable and interesting temporal and categorical patterns regarding user online behavior and interests. To discover the valuable behavior patterns from the individual’s browsing data, different web usage mining techniques have been used. Finally, we demonstrate interactive visualizations for the analysis and monitoring of web browsing behavior patterns with the goal of providing the individual with detailed understanding of his/her behavior. We also present a small-scale study including university students, which proves the importance of our work. Keywords—Pattern discovery; visualization; behavior modeling; web usage mining; browsing I. I NTRODUCTION Quantified self-movement incorporates digital technology to acquire data on various aspects of an individual’s life with an aim to improve self-awareness and human performance. People want to be self-aware, self-knowledgeable in order to improve their performance and outcomes. Today, technology logs almost everything we do with the aim to measure all aspects of our daily lives. While using digital services, in- dividuals leave behind traces of their activities that offer an opportunity to gain insights about themselves, their interests and their behavior. Web usage mining is the major research area in data mining that facilitates to predict the individuals browsing behaviors and infer their interests by analyzing the behavior patterns. It consists of three phases: preprocessing, pattern discovery and pattern analysis. Preprocessing is required to convert the raw data into a meaningful form useful for efficient processing. Pattern discovery includes techniques to extract the pattern and encompasses statistical analysis, sequential pattern mining, path analysis, association rule mining, classification, and clustering [1]. For analysis of patterns, we can use visu- alization which allows to understand and analyze the patterns in an intuitive way. There are many information visualization techniques that have been developed over the last few years that can deal with wide range of data [2]. A. Problem Context Life has become so much fast and busy these days that even we do not have time to pay attention to our true selves. The disease of being busy is spiritually destructive to our health and well-being leading us towards stress, depression, and anxiety. Many people waste time on activities that keep them busy but not productive. They spend most of their time in surfing the Web without even noticing how much time has been wasted and how badly this behavior can affect their performance and productivity. According to the research in 2017 [3], the Internet is capturing more and more of our time each day. Daily average of Internet usage has increased to 6.15 hours and time spent on social networking is also growing day by day. In order to monitor how individuals spend their time online, productively, there is need for an automated time management application that can track their online activities and help them in discovering their good and bad behavior so that they can make changes when necessary. Thus, several self-tracking applications have been developed that bring self-awareness among individuals, help in making valuable decisions, improve their judgment and bring positive changes in their behavior and life. However, considering the limitations of existing applications (discussed in the next section) and the need for improved means for self-awareness, we present our research approach and findings in this article. B. Objectives and Scope The main objective of our research is to develop a system for analysis of web-usage behavior patterns using interac- tive visualization techniques to promote self-reflection among users. Moreover, the system should be able to present the behavior from different perspectives using temporal and cate- gorical dimensions. Following are the objectives of our research work: • Development of framework for gathering and process- ing of web usage data. www.ijacsa.thesai.org 490 | Page