Abstract
The website navigation patterns can be searched and analyzed with the introduction of the new methodology. The user navigation path is stored as a sequence of URL categories in web server. The approaches followed are to separate the users and sessions from the web log files and acquiring the necessary patterns for web personalization. The clustering concept is used for grouping the necessary patterns in separate groups. The approaches used for clustering of navigation patterns are done with improvised particle swarm optimization technique which divides users depends on the order in which they request web pages. This approach mines the web log files which are resultant from the web users while interacting with web pages for a particular period of web sessions. The work carried with an optimized method of particle swarm optimization-K-Harmonic means to cluster the similar users based on their navigation pattern. Particle swarm optimization-K-Harmonic method is used to discover or extract user
Highlights
Web mining is the technique of data mining for acquiring knowledge from large databases
The extraction of necessary access logs information for web personalization process undergoes many processes which coincide with the general data mining flow of preprocessing, identifying patterns and analyzing collected patterns
The contribution of many researchers in web mining shows the general three categorization of web mining known as structure mining, content mining and usage mining
Summary
Web mining is the technique of data mining for acquiring knowledge from large databases. The extraction of necessary access logs information for web personalization process undergoes many processes which coincide with the general data mining flow of preprocessing, identifying patterns and analyzing collected patterns. The general cleaning of unwanted data from web access log files are completed in the preprocessing stage. The web access log files attain a new format after the preprocessing stage which is more suitable for identifying patterns. Analyzing the patterns is the final stage of every web usage mining process and it is possible only after generating the specific rules by applying algorithms in identifying the patterns. The formation of grouping is done by applying clustering algorithms Researchers have shown their interest in using many kinds of techniques such as k-mean, c-mean and ant based clustering in the pattern discovery process (Nicolas et al, 2003). The technique followed for the clustering process in the proposed framework follows some of clustering techniques in common and differ in introducing Particle swarm optimization-K-Harmonic technique for classifying the patterns
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