Evaluating Non-redundant Rules of Various Sequential Rule Mining Algorithms

Autor:	Nesma Youssef, Amira Abdelwahab, Hatem Abdul-Kader
Rok vydání:	2020
Předmět:	Measure (data warehouse) Computer science Sequential Pattern Mining Bit array Algorithm Execution time Field (computer science) Sequential rule mining
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030586683 AISI
DOI:	10.1007/978-3-030-58669-0_39
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fda79b93579b54082a5f31cf544e88c6 https://doi.org/10.1007/978-3-030-58669-0_39 Zobrazit plný text záznamu