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An Improved Algorithm for Extracting Frequent Gradual Patterns
Volume 35, Issue 3 (2024), pp. 577–600
Pub. online: 24 July 2024      Type: Research Article      Open accessOpen Access
Received
1 April 2023
Accepted
1 June 2024
Published
24 July 2024

Abstract

Frequent gradual pattern extraction is an important problem in computer science widely studied by the data mining community. Such a pattern reflects a co-variation between attributes of a database. The applications of the extraction of the gradual patterns concern several fields, in particular, biology, finances, health and metrology. The algorithms for extracting these patterns are greedy in terms of memory and computational resources. This clearly poses the problem of improving their performance. This paper proposes a new approach for the extraction of gradual and frequent patterns based on the reduction of candidate generation and processing costs by exploiting frequent itemsets whose size is a power of two to generate all candidates. The analysis of the complexity, in terms of CPU time and memory usage, and the experiments show that the obtained algorithm outperforms the previous ones and confirms the interest of the proposed approach. It is sometimes at least 5 times faster than previous algorithms and requires at most half the memory.

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Biographies

Kenmogne Edith Belise
ebkenmogne@gmail.com

E.B. Kenmogne received her master and PhD degrees in computer science at the Department of Mathematics and Computer Science of the Faculty of Sciences of the University of Dschang (Cameroon) in 2012 and 2018, respectively. Her current research interests include data mining and artificial intelligence. In January 2020, she started working at the University of Dschang as a lecturer. In December 2021, she was promoted to the rank of senior lecturer.

Tetakouchom Idriss
https://orcid.org/0000-0003-3413-0981
itetakouchom@gmail.com

I. Tetakouchom received his master degree in computer science from the Department of Mathematics and Computer Science at the Faculty of Sciences of the University of Dschang in 2019, where he is now a PhD candidate. His main research topic is knowledge discovery.

Tayou Djamegni Clémentin
dtayou@gmail.com

C. Tayou Djamegni is a full professor of computer science at the University of Dschang (Uds). He obtained the DEA, the Doctorat de Troisième Cycle, and the Doctorat d’État at the Department of Computer Science of the Faculty of Sciences of the University of Yaoundé I in 1995, 1997 and 2005, respectively. From December 2007 to March 2018, he headed the Department of Mathematics and Computer Science at the Faculty of Sciences of the Uds. In this position, he initiated and coordinated the design and implementation of the first Master’s and doctoral programs of the Uds in computer science and mathematics. He also launched and coordinated the creation of the first computer science research laboratory at Uds LIFA, later renamed URIFIA. From March 2018 to this day, he is head of the Computer Engineering Department at the Fotso Victor University Institute of Technology. He supervised fifteen doctoral theses in computer science. He is a member of the Editorial Board of Informatics in Medicine Unlocked and African Revue in Informatics and Mathematics Applied. He won four third prizes, one second prize and four first prizes at SAT competitions, and one third prize at EDA CHALLENGE 2021. His research interests include sensor networks, knowledge discovery, distributed algorithms, cloud computing, artificial intelligence and security.

Nkambou Roger
nkambou.roger@uqam.ca

R. Nkambou received his PhD degree in computer science from the Université de Montréal in 1996. He is a full professor of computer science at the Université du Québec à Montréal and the Director of the Artificial Intelligence Research Center (http://gdac.uqam.ca/CRIA). His research interests include machine learning, knowledge representation, intelligent tutoring systems, ontology engineering, data mining, and affective computing. He is an associate editor of Frontiers in Artificial Intelligence. He also serves as a senior member of the program committees for important international conferences such as EDM, ITS and AIED.

Tabueu Fotso Laurent Cabrel
laurent.tabueu@gmail.com

L. Tabueu Fotso received his master and PhD degree in computer science from the Department of Mathematics and Computer Science at the Faculty of Sciences of the University of Dschang in 2017 and 2023, respectively. His main research topic is knowledge discovery.


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Keywords
gradual pattern frequent pattern candidate binary matrix mining

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