Web content mining using web design patterns

Kudělka, Miloš; Snášel, Václav; Lehecka, Ondrej; El-Qawasmeh, Eyas

doi:10.1109/iri.2008.4583035

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The new algorithm initially constructs a frequent access pattern tree and then mines users' frequent access patterns on the tree. Kudelka et al (2008) have created own Pattrio method of pattern detection on web pages. The detected patterns on web pages illustrate the web page structural design from the external viewpoint of the user.…”

Section: Pattern Discoverymentioning

confidence: 99%

A Novel Approach for User Navigation Pattern Discovery and Analysis for Web Usage Mining

Vellingiri¹,

Kaliraj²,

Satheeshkumar³

et al. 2015

Journal of Computer Science

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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, prefetching, 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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Section: Pattern Discoverymentioning

confidence: 99%