Probabilistic and exact frequent subtree mining in graphs beyond forests

Author:

Welke PascalORCID,Horváth TamásORCID,Wrobel Stefan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference38 articles.

1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A.I. (1996). Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining (pp. 307–328). AAAI/MIT Press.

2. Akutsu, T. (1993). A polynomial time algorithm for finding a largest common subgraph of almost trees of bounded degree. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 76(9), 1488–1493.

3. Arnborg, S., Corneil, D. G., & Proskurowski, A. (1987). Complexity of finding embeddings in a k-tree. SIAM Journal on Algebraic Discrete Methods, 8(2), 277–284. https://doi.org/10.1137/0608024 .

4. Bringmann, B., Zimmermann, A., De Raedt, L., & Nijssen, S. (2006). Don’t be afraid of simpler patterns. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou (Eds.), European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) Proceedings, Lecture Notes in Computer Science (Vol. 4213, pp. 55–66). Springer. https://doi.org/10.1007/11871637_10 .

5. Chi, Y., Muntz, R. R., Nijssen, S., & Kok, J. N. (2005). Frequent subtree mining—An overview. Fundamenta Informaticae, 66(1–2), 161–198.

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