A Novel Approach for User Navigation Pattern Discovery and Analysis for Web Usage Mining
- 1 , India
Copyright: © 2020 J. Vellingiri, S. Kaliraj, S. Satheeshkumar and T. Parthiban. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Websites on the internet are useful source of information in our day-to-day activity. Web Usage Mining (WUM) is one of the major applications of data mining, artificial intelligence and so on to the web data to predict the user's visiting behaviours and obtains their interests by analyzing the patterns. WUM has turned out to be one of the considerable areas of research in the field of computer and information science. Weblog is one of the major sources which contain all the information regarding the users visited links, browsing patterns, time spent on a page or link and this information can be used in several applications like adaptive web sites, personalized services, customer profiling, pre-fetching, creating attractive web sites etc. WUM consists of preprocessing, pattern discovery and pattern analysis. Log data is typically noisy and unclear, so preprocessing is an essential process for effective mining process. In the preprocessing phase, the data cleaning process includes removal of records of graphics, videos, format information, records with the failed HTTP status code and robots cleaning. In the second phase, the user behaviour is organized into a set of clusters using Weighted Fuzzy-Possibilistic C-Means (WFPCM), which consists of "similar" data items based on the user behaviour and navigation patterns for the use of pattern discovery. In the third phase, classification of the user behaviour is carried out for the purpose of analyzing the user behaviour using Adaptive Neuro-Fuzzy Inference System with Subtractive Algorithm (ANFIS-SA). The performance of the proposed work is evaluated based on accuracy, execution time and convergence behaviour using anonymous microsoft web dataset.
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- Web Usage Mining (WUM)
- Robots Cleaning
- Weighted Fuzzy-Possibilistic C-Means (WFPCM)
- Adaptive Neuro-Fuzzy Inference System with Subtractive Algorithm (ANFIS-SA)
- Modified Levenberg-Marquardt Algorithm