Abstract
We study the problem of mining closed frequent subtrees from tree databases that are updated regularly over time. Closed frequent subtrees provide condensed and complete information for all frequent subtrees in the database. Although mining closed frequent subtrees is in general faster than mining all frequent subtrees, this is still a very time consuming process, and thus it is undesirable to mine from scratch when the change to the database is small. The set of previous mined closed subtrees should be reused as much as possible to compute new emerging subtrees. We propose, in this paper, a novel and efficient incremental mining algorithm for closed frequent labeled ordered trees. We adopt a divide-and-conquer strategy and apply different mining techniques in different parts of the mining process. The proposed algorithm requires no additional scan of the whole database while its memory usage is reasonable. Our experimental study on both synthetic and real-life datasets demonstrates the efficiency and scalability of our algorithm.
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References
Abiteboul, S., Buneman, P., Suciu, D.: Data on the Web. Morgan Kaufmann, San Francisco (2000)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. the ACM SIGMOD Intl. Conf. on Management of Data, pp. 207–216 (1993)
Asai, T., Abe, K., Kawasoe, S., Arimura, H., Sakamoto, H., Arikawa, S.: Efficient Substructure Discovery From Large Semi-structured Data. In: Proc. the Second SIAM International Conference on Data Mining (SDM 2002), pp. 158–174 (2002)
Asai, T., Arimura, H., Abe, K., Kawasoe, S., Arikawa, S.: Online Algorithms for Mining Semi-structured Data Stream. In: Proc. IEEE International Conference on Data Mining (ICDM 2002), pp. 27–34 (2002)
Bifet, A., Gavalda, R.: Mining Adaptively Frequent Closed Unlabeled Rooted Trees in Data Streams. In: Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-08), pp. 34–42 (2008)
Cheng, H., Yan, X., Han, J.: IncSpan: Incremental Mining of Sequential Patterns in Large Database. In: Proc. the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-04), pp. 527–532 (2004)
Chi, Y., Yang, Y., Xia, Y., Muntz, R.R.: CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 63–73. Springer, Heidelberg (2004)
Chi, Y., Muntz, R.R., Nijssen, S., Kok, J.N.: Frequent Subtree Mining - An Overview. In: Fundamenta Informaticae, vol. 66, pp. 161–198 (2005)
Hashimoto, K., Takigawa, I., Shiga, M., Kanehisa, M., Mamitsuka, H.: Mining Significant Tree Patterns in Carbohydrate Sugar Chains. In: Proc. the 7th European Conference on Computational Biology, pp. 167–173 (2008)
Hsieh, M., Wu, Y., Chen, A.: Discovering frequent tree patterns over data streams, in Proc. SIAM International Conference on Data Mining (SDM 2006), pp. 629-633 (2006)
Nijssen, S., Kok, J.N.: Efficient Discovery of Frequent Unordered Trees. In: Proc. the First International Workshop on Mining Graphs, Trees and Sequences (MGTS2003), in conjunction with ECML/PKDD 2003, pp. 55-64 (2003)
Termier, A., Rousset, M.C., Sebag, M.: Dryade: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 543–546. Springer, Heidelberg (2004)
Wang, D., Peng, G.: A New Marketing Channel Management Strategy Based on Frequent Subtree Mining. Communications of the IIMA 7(1), 49–54 (2007)
Zaki, M.J.: Efficiently Mining Frequent Trees in a Forest. In: Proc. the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 71–80 (2002)
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Nguyen, V.A., Yamamoto, A. (2010). Incremental Mining of Closed Frequent Subtrees. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds) Discovery Science. DS 2010. Lecture Notes in Computer Science(), vol 6332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16184-1_25
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DOI: https://doi.org/10.1007/978-3-642-16184-1_25
Publisher Name: Springer, Berlin, Heidelberg
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