Open AccessAn Algorithm for Mining High Utility Sequential Patterns with Time Interval
Author(s) -
Tran Huy Duong,
János Demetrovics,
Vũ Đức Thi,
Nguyễn Tất Thắng
Publication year - 2019
Publication title -
cybernetics and information technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.272
H-Index - 17
eISSN - 1314-4081
pISSN - 1311-9702
DOI - 10.2478/cait-2019-0032
Subject(s) - computer science , interval (graph theory) , sequence (biology) , sequential pattern mining , data mining , time sequence , property (philosophy) , algorithm , closure (psychology) , value (mathematics) , sequence database , machine learning , mathematics , philosophy , biochemistry , genetics , chemistry , epistemology , combinatorics , economics , gene , market economy , biology
Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. In real world applications, the time intervals between elements are also very important. However, recent HUSP mining algorithms cannot extract sequential patterns with time intervals between elements. Thus, in this paper, we propose an algorithm for mining high utility sequential patterns with the time interval problem. We consider not only sequential patterns’ utilities, but also their time intervals. The sequence weight utility value is used to ensure the important downward closure property. Besides that, we use four time constraints for dealing with time interval in the sequence to extract more meaningful patterns. Experimental results show that our proposed method is efficient and effective in mining high utility sequential pattern with time intervals.