A Simple Yet Efficient Approach for Maximal Frequent Subtrees Extraction from a Collection of XML Documents
Recently, XML is penetrating virtually all areas of computer science and information technology, and is bringing about an unprecedented level of data exchange among heterogeneous data storage systems. With the continuous growth of online information stored, presented and exchanged using XML, the discovery of useful information from a collection of XML documents is currently one of the main research areas occupying the data mining community. The mostly used approach to this task is to extract frequently occurring subtree patterns in trees. However, the number of frequent subtrees usually grows exponentially with the size of trees, and therefore, mining all frequent subtrees becomes infeasible for a large tree size. A more practical and scalable approach is to use maximal frequent subtrees, the number of which is much smaller than that of frequent subtrees. Handling the maximal frequent subtrees is an interesting challenge, and represents the core of this paper. We present a novel, conceptually simple, yet effective approach that discovers maximal frequent subtrees without generation of candidate subtrees from a database of XML trees. The beneficial effect of our approach is that it not only reduces significantly the number of rounds for infrequent tree pruning, but also eliminates totally each round for candidate generation by avoiding time consuming tree join operations or tree enumerations.
KeywordsParent Node Mining Algorithm Minimum Support Semistructured Data Data Mining Community
Unable to display preview. Download preview PDF.
- 1.Abiteboul, S., Buneman, P., Suciu, D.: Data on the Web: From Relations to Semistructured Data and XML, 1st edn. Morgan Kaufmann, San Francisco (1999)Google Scholar
- 2.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 12th International Conference on Very Large Databases, pp. 487–499 (1994)Google Scholar
- 3.Asai, T., Abe, K., Kawasoe, S., Arimura, H., Sakamoto, H., Arikawa, S.: Efficient substructure discovery from large semi-structured data. In: Proceedings of the 2nd SIAM International Conference on Data Mining, pp. 158–174 (2002)Google Scholar
- 4.Buneman, P.: Semistructured data. In: Proceedings of the 16th ACM SIGACT-SIGMOD-SIGART symposium on Principles of databases systems, pp. 117–121 (1997)Google Scholar
- 6.Chi, Y., Xia, Y., Yang, Y., Muntz, R.R.: Mining closed and maximal frequent subtrees from databases of labeled rooted trees. IEEE Trans. Knowledge and Data Engineering 17(3), 190–202 (2005)Google Scholar
- 7.Chi, Y., Yang, Y., Muntz, R.R.: HybridTreeMiner: An efficient algorithm for mining frequent rooted trees and free trees using canonical forms. In: The 16th International Conference on Scientific and Statistical Database Management, pp. 11–20 (2004)Google Scholar
- 10.Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of IEEE International Conference on Data Mining, pp. 313–320 (2001)Google Scholar
- 11.Kilpeäinen, P.: Tree matching problems with applications to structured text databases. PhD thesis in University of Helsinki (1992)Google Scholar
- 14.Termier, A., Rousset, M.-C., Sebag, M.: TreeFinder: a First step towards XML data mining. In: Proceedings of IEEE International Conference on Data Mining, pp. 450–457 (2002)Google Scholar
- 15.Wang, K., Liu, H.: Schema discovery for semistructured data. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 271–274 (1997)Google Scholar
- 16.Xiao, Y., Yao, J.-F., Li, Z., Dunham, M.H.: Efficient data mining for maximal frequent subtrees. In: Proceedings of IEEE Internation Conference on Data Mining, pp. 379–386 (2003)Google Scholar
- 17.Zaki, M.J.: Efficiently mining frequent trees in a forest. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 71–80 (2002)Google Scholar