Abstract

The data mining techniques help discover hidden knowledge from a huge database. In the pattern mining field, the main goal is to discover interesting patterns in large databases. The sequential pattern mining technique is specialized for discovering sequential patterns with only one measure called support. It is not sufficient and misleading for the user. Sequential rule mining is a good solution that takes another measure into an account called confidence. This paper presents a comparative analysis between two sequential rule mining algorithms, namely non-redundant with dynamic bit vector (NRD-DBV), and TRuleGrowth algorithm. The study clarifies the execution time, the number of rules, and the memory usage for each algorithm. In addition, exposure to the most proper field for each algorithm to achieve elevated efficiency.

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