A novel algorithm for extracting frequent gradual patterns

• Main Authors: Tayou Djamegni Clémentin, Tabueu Fotso Laurent Cabrel, Kenmogne Edith Belise
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: Machine Learning with Applications
• Subjects: Pattern mining; Pruning; Search space; Gradual support; Lattice; Adjacency matrix
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666827021000347

The extraction of frequent gradual pattern is an important problem in computer science and largely studied by the scientist’s community of research in data mining. A frequent gradual pattern translates a recurrent co-variation between the attributes of a database. Many applications issues from many domains, such as economy, health, education, market, bio-informatics, astronomy or web mining, are based on the extraction of frequent gradual patterns. Algorithms to extract frequent gradual patterns in the large databases are greedy in CPU time and memory space. This raises the problem of improving the performances of these algorithms. This paper presents a technique for improving the performance of frequent gradual pattern extraction algorithms. The exploitation of this technique leads to a new, more efficient algorithm called SGrite. The experiments carried out confirm the interest of the proposed technique.