An Empirical Analysis of Web Navigation Prediction Techniques

An Empirical Analysis of Web Navigation Prediction Techniques

Honey Jindal (Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India) and Neetu Sardana (Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India)
Copyright: © 2017 |Pages: 14
DOI: 10.4018/jcit.2017010101
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With the advancement of Information Technology, web is growing rapidly and it has became necessary part of our daily lives. It is mandate to study the navigation behavior of the user to improve the quality of web site design for personalization and further recommendation. Analysis of web navigation behavior heavily relies on navigational models. This paper is an effort to give insights of current state-of-the-art techniques used for web navigation prediction. These navigation models are broadly classified into three categories: sequential mining, classification and clustering. Analytical analysis is performed on all the categories used in web navigation prediction. Further empirical analysis is performed on popular techniques of each category Markov Model (sequential mining), Support vector machine (classification) and K-means (clustering) on the common platform to measure the effectiveness of these techniques.
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Predicting the web navigation behaviour of the user has attracted the attention of many researchers and practitioners. There are many papers which present the broad survey on web usage mining. Surveys (Srivastava et al., 2000; Hu Chen, 2003; Pani & Saroj, 2011; Singh, Brijendra, 2010) present an overview of web usage mining algorithms and majorly focused on the pattern extraction techniques. In these papers, the phases required in pattern construction and the general data mining techniques are discussed.(Srivastava et al., 2000; Prasad, 2015; Facca et al., 2005) presented the survey in the context of various dimensions of web usage mining like data sources, navigation techniques (like association rule mining, sequential mining and clustering), and software tools used for analysis, applications and security.

(Renata Iváncsy et al., 2006) focused on frequent pattern mining techniques. The paper provides the brief summary of how frequent patterns can be obtained from the web logs. (Baraskar et al., 2015; Neve, 2013; Kapil, 2014) surveyed the sequential mining techniques and does not provide the insights of other WNP techniques. (Kumar et al., 2014) done a theoretical underpinning of user navigation using Markov based models. Observations are highlighted based on prior facts. Models are not implemented in a common platform to provide better insights. Most of these papers surveyed specifically on sequential mining only.

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