A novel algorithm for extracting frequent gradual patterns

• Published in: Machine learning with applications Vol. 5; p. 100068
• Main Authors: Clémentin, Tayou Djamegni, Cabrel, Tabueu Fotso Laurent, Belise, Kenmogne Edith
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.09.2021; Elsevier
• Subjects: Adjacency matrix; Gradual support; Lattice; Pattern mining; Pruning; Search space; Pruning; Gradual support; Adjacency matrix; Pattern mining; Search space; Lattice
• ISSN: 2666-8270; 2666-8270
• DOI: 10.1016/j.mlwa.2021.100068

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. •A method which optimizes the mining cost of frequent gradual patterns is proposed.•In the method, gradual support is based on the so-called precedence graph approach.•The search space and the computing time of the support are optimized.•Experiments on different types of data sets confirm the effectiveness of the method.