Mining Generalized Closed Patterns from Multi-graph Collections

• Published in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications Vol. 10657; pp. 10 - 18
• Main Authors: Acosta-Mendoza, Niusvel, Gago-Alonso, Andrés, Carrasco-Ochoa, Jesús Ariel, Martínez-Trinidad, José Francisco, Medina-Pagola, José Eladio
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Approximate graph mining; Frequent multi-graph mining; Generalized frequent patterns
• ISBN: 9783319751924; 3319751921
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-75193-1_2

Frequent approximate subgraph (FAS) mining has become an important technique into the data mining. However, FAS miners produce a large number of FASs affecting the computational performance of methods using them. For solving this problem, in the literature, several algorithms for mining only maximal or closed patterns have been proposed. However, there is no algorithm for mining FASs from multi-graph collections. For this reason, in this paper, we introduce an algorithm for mining generalized closed FASs from multi-graph collections. The proposed algorithm obtains more patterns than the maximal ones, but less than the closed one, covering patterns with small frequency differences. In our experiments over two real-world multi-graph collections, we show how our proposal reduces the size of the FAS set.