An efficient and effective algorithm for mining top-rank-k frequent patterns

• Published in: Expert systems with applications 2015-01, Vol.42 (1), p.156-164
• Main Authors: Huynh-Thi-Le, Quyen, Le, Tuong, Vo, Bay, Le, Bac
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Exact sciences and technology; Expert systems; Information systems. Data bases; Memory organisation. Data processing; Mining; N-list; Pattern analysis; Pattern mining; Ranking; Software; Top-rank-k frequent patterns
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.07.045

•Using N-list structure for mining top-rank-k frequent patterns effectively.•Subsume concept was also used to speed up the runtime of the mining process.•The experiment was conducted to show the effectiveness of the proposed algorithm. Frequent pattern mining generates a lot of candidates, which requires a lot of memory usage and mining time. In real applications, a small number of frequent patterns are used. Therefore, the mining of top-rank-k frequent patterns, which limits the number of mined frequent patterns by ranking them in frequency, has received increasing interest. This paper proposes the iNTK algorithm, which is an improved version of the NTK algorithm, for mining top-rank-k frequent patterns. This algorithm employs an N-list structure to represent patterns. The subsume concept is used to speed up the process of mining top-rank-k patterns. The experiments are conducted to evaluate iNTK and NTK in terms of mining time and memory usage for eight datasets. The experimental results show that iNTK is more efficient and faster than NTK.