An Efficient Fuzzy Clustering Algorithm for Mining User Session Clusters on Web Log Data
Data mining is extremely vital to get important
information from the web. Additionally, web usage
mining (WUM) is essential for companies. WUM
permits organizations to create rich information related
to the eventual fate of their commercial capacity. The
utilization of data that is assembled by Web Usage
Mining gives the organizations the capacity to deliver
results more compelling to their organizations and
expanding of sales. Client access patterns can be mined
from web access log information using Web Usage
Mining (WUM) techniques. Because there are so many
end-user sessions and URL resources, the size of web
user session data is enormous. Human communications
and non-deterministic browsing patterns increment
equivocalness and dubiousness of client session
information. The fuzzy set-based approach can solve
most of the challenges listed above. This paper proposes
an efficient Fuzzy Clustering algorithm for mining
client session clusters from web access log information
to find the groups of client profiles. In addition, the
methodologies to preprocess the net log data as well as
data cleanup client identification and session
identification are going to be mentioned. This
incorporates the strategy to do include choice (or
dimensionality decrease) and meeting weight task
feature evaluation and dimensionality reduction to improve the quality of web
usage mining results. arXiv preprint arXiv:1509.00690.
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