VMSP: Efficient Vertical Mining of Maximal Sequential Patterns
Sequential pattern mining is a popular data mining task with wide applications. However, it may present too many sequential patterns to users, which makes it difficult for users to comprehend the results. As a solution, it was proposed to mine maximal sequential patterns, a compact representation of the set of sequential patterns, which is often several orders of magnitude smaller than the set of all sequential patterns. However, the task of mining maximal patterns remains computationally expensive. To address this problem, we introduce a vertical mining algorithm named VMSP (Vertical mining of Maximal Sequential Patterns). It is to our knowledge the first vertical mining algorithm for mining maximal sequential patterns. An experimental study on five real datasets shows that VMSP is up to two orders of magnitude faster than the current state-of-the-art algorithm.
Keywordsvertical mining maximal sequential pattern mining candidate pruning
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Ramakrishnan, S.: Mining sequential patterns. In: Proc. 11th Intern. Conf. Data Engineering, pp. 3–14. IEEE (1995)Google Scholar
- 2.Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proc. 8th ACM Intern. Conf. Knowl. Discov. Data Mining, pp. 429–435. ACM (2002)Google Scholar
- 4.Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast Vertical Sequential Pattern Mining Using Co-occurrence Information. In: Proc. 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining. LNCS (LNAI). Springer (2014)Google Scholar
- 7.Guan, E.-Z., Chang, X.-Y., Wang, Z., Zhou, C.-G.: Mining Maximal Sequential Patterns. In: Proc. 2nd Intern. Conf. Neural Networks and Brain, pp. 525–528 (2005)Google Scholar
- 8.Lin, N.P., Hao, W.-H., Chen, H.-J., Chueh, H.-E., Chang, C.-I.: Fast Mining Maximal Sequential Patterns. In: Proc. of the 7th Intern. Conf. on Simulation, Modeling and Optimization, Beijing, China, September 15-17, pp. 405–408 (2007)Google Scholar
- 9.Luo, C., Chung, S.: Efficient mining of maximal sequential patterns using multiple samples. In: Proc. 5th SIAM Intern. Conf. Data Mining, Newport Beach, CA (2005)Google Scholar
- 10.Lu, S., Li, C.: Apriori Adjust: An Efficient Algorithm for Discovering the Maximum Sequential Patterns. In: Proc. Intern. Workshop Knowl. Grid and Grid Intell. (2004)Google Scholar