Open AccessHIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM
Author(s) -
Tran Huy Duong,
Nguyễn Tất Thắng,
Vũ Đức Thi,
Tran The Anh
Publication year - 2020
Publication title -
journal of computer science and cybernetics (vietnam academy of science and technology)/journal of computer science and cybernetics
Language(s) - English
Resource type - Journals
eISSN - 2815-5939
pISSN - 1813-9663
DOI - 10.15625/1813-9663/36/1/14398
Subject(s) - sequential pattern mining , interval (graph theory) , computer science , sequence (biology) , data mining , algorithm , artificial intelligence , mathematics , combinatorics , biology , genetics
High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.