Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 47, Pages: 1-8
K. Poongothai1 *, M. Mathivanan2 , T. Senthilkumaran2 and S. Revathi3
1 Selvam College of Technology, Namakkal - 637003, Tamil Nadu, India; [email protected] 2 A.C.S. College of Engineering, Bengaluru - 560074, Karnataka, India; [email protected], [email protected] 3B. S. Abdur Rahman University, Chennai - 600048, Tamil Nadu, India; [email protected]
*Author for correspondence
Selvam College of Technology, Namakkal - 637003, Tamil Nadu, India; [email protected]
Objectives: To deliver excellent performance when compared to existing method Mining Comprehensible Classification Rules for Time-Series (MCCR-TS) using zoo dataset collected from UCI repository comprising of more than 1000 records. Methods/Analysis: Research works on Time-series data has been evolving as a new trend in current scenarios due to the wide range of applications involved in it. One of the widely researched topics is the web usage mining and effective evaluation of time-series movements and transaction associated with them. Most of the studies conducted earlier, focused on identifying the time-series data from the entire logs. However, this type of patterns may not be accurate enough for evaluation due to differentiated behaviors of user patterns are not taken into consideration. Findings: In this paper, we examine the time series for temporal data using integrated cluster-based rule induction mining, by presenting a novel algorithm, namely, Integrated Cluster-based Rule Induction Mining for Time-Series analysis (ICRIM-TS). The algorithm discovers temporal data and the similarities between users are evaluated by the proposed measure, Time Series. To our best knowledge, this is the first work on mining and identification of user behaviors based on the time series data with preferences being given for both user relations and temporal data at the same time. Improvement: Through experimental evaluation, under various settings, the performance of web usage mining is evaluated in terms of precision of users, error threshold value and number of rules extracted.
Keywords: Cluster-based Rule Induction Mining, Temporal Data, Time-series Analysis, User Relations, Web Usage Mining
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