Preprocessing: A Prerequisite for Discovering Patterns in Web Usage Mining Process
Abstract
Web log data is usually diverse and voluminous.
This data must be assembled into a consistent, integrated and
comprehensive view, in order to be used for pattern discovery.
Without properly cleaning, transforming and structuring the
data prior to the analysis, one cannot expect to find
meaningful patterns. As in most data mining applications, data
preprocessing involves removing and filtering redundant and
irrelevant data, removing noise, transforming and resolving
any inconsistencies. In this paper, a complete preprocessing
methodology having merging, data cleaning, user/session
identification and data formatting and summarization
activities to improve the quality of data by reducing the
quantity of data has been proposed. To validate the efficiency
of
the proposed preprocessing methodology, several
experiments are conducted and the results show that the
proposed methodology reduces the size of Web access log files
down to 73-82% of the initial size and offers richer logs that
are structured for further stages of Web Usage Mining (WUM).
So preprocessing of raw data in this WUM process is the
central theme of this paper.
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