Title: Grouping of users based on user navigation behaviour using supervised association rule tree mining
Authors: R. GeethaRamani; P. Revathy; B. Lakshmi
Addresses: Department of Information Science and Technology, CEG, Anna University, Chennai, India ' Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India ' Department of Information Science and Technology, CEG, Anna University, Chennai, India
Abstract: In this internet world, an increased interest of users in search of World Wide Web results in wide relevance of web mining, an application of data mining. Clustering has been widely used for web usage mining. Finding initial cluster center and specifying the number of clusters are the major challenges, which are overcome in this work by grouping of users based on the target class value. The benchmark dataset MSNBC is collected for the entire day of September 28, 1999. Supervised association rule tree mining is used to find frequent itemset for the targeted class value and thus generating 'if then rules'. Users are automatically clustered based on the rules satisfying the ground truth, resulting in 36 clusters in two iterations. The results revealed that the renowned clustering algorithms such as K-means takes 22 iterations for forming 36 clusters, wherein the proposed work generates 36 clusters in two iterations.
Keywords: clustering algorithm; data mining; MSNBC; web usage mining; supervised association rule tree mining.
International Journal of Reasoning-based Intelligent Systems, 2018 Vol.10 No.3/4, pp.307 - 315
Received: 09 Sep 2017
Accepted: 06 Dec 2017
Published online: 19 Nov 2018 *