METHOD FOR SEARCHING SEQUENTIAL PATTERNS OF USER'S BEHAVIOR ON THE INTERNET
Abstract
One of the important tasks of data mining is to isolate patterns and detect related events in
sequential data based on the analysis of sequential patterns. The article examines the possibility of
using sequential patterns to analyze the events of search and cognitive activity of users when interacting
with Internet resources of an open information and educational environment. Searching
for sequential patterns is a complex computational task whose goal is to retrieve all frequent sequences
representing potential relationships within elements from a transactional database of
sequences of search activity events with a given minimum support. To solve it, the article proposes
a method for searching for patterns in sequences of events to detect hidden patterns that indicate
possible levels of vulnerability when performing information search tasks in the Internet space.
A mathematical model of user behavior in a search session based on the theory of sequential patterns
is described. To improve the computational efficiency of the method, a modified algorithm
for generating sequential patterns has been developed, at the first stage of which AprioriAll is
performed, which forms frequent candidate sequences of all possible lengths, and at the second
stage, a genetic algorithm for optimizing the input parameters of the feature space of the generated
set to search for maximum patterns. A series of computational experiments were carried out on
test data from the MSNBC corpus, the SPMF open source data mining library. The comparative
analysis was carried out with the VMSP and GSP algorithms. The research results confirmed the
efficiency of the search for maximum sequential patterns by the proposed algorithm in terms of the
execution time and the number of extracted patterns. The results of the experimental studies of the
method showed that to increase the stability and accuracy of the work, the sample size obtained as
a result of the GA operation will reduce the required number of scans of the pattern database,
providing acceptable computational costs comparable to the VMSP algorithm and the GSP algorithm
that exceeds the search time for sequential patterns. an average of more than 150 %.
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