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 %.