Abstract
Mining frequent pattern from databases is useful for knowledge discovery. In this paper, we propose modified CP-Tree, which scans entire transactions only once and constructs the tree by inserting the transactions one by one. The constructed tree consists of an item list along with its occurrence. In addition, a sorted order of items with its frequency of occurrence is maintained and based on the sorted value, the tree is dynamically rearranged. In rearranging phase, the nodes are rearranged in each branch based on sorted order of items. Each path of the branch is removed from the tree, sorted based on sorted order of items and inserted back as a branch into the tree. We have evaluated the performance of the proposed modified tree on benchmark databases such as CHESS, MUSHROOM and T10I4D100K. It is observed that the time taken for extracting frequent item from the tree is encouraging compared to conventional CP-Tree.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings. ACM-SIGMOD International Conference on Management of Data (SIGMOD), Washington, DC, pp. 207–216 (1993)
Ayan, N.F., Tansel, A.U., Akrun, E.: An efficient algorithm to update large itemsets with early pruning. In: Proceedings of the Fifty ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 287–291 (1999)
Cheung, D.W., Lee, S.D., Kao, B.: A general incremental technique for maintaining discovered association rules. In: Proceedings of the Fifth International Conference on Database Systems for Advanced Applications, pp. 185–194 (1997)
Cheung, W., Za, O.R.: Incremental mining of frequent patterns without candidate generation or support constraint. In: Proceedings of the Seventh International Database Engineering and Applications Symposium, IDEAS 2003 (2003)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1–12 (2000)
Hong, T.-P., Lin, C.-W., Wu, Y.-L.: Incrementally fast updated frequent pattern trees. Expert Systems with Applications 34(4), 2424–2435 (2008)
Koh, J.-L., Shieh, S.-F.: An efficient approach for maintaining association rules based on adjusting FP-tree structures. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 417–424. Springer, Heidelberg (2004)
Leung, C.K., Khan, Q.I., Li, Z., Hoque, T.: CanTree: a canonical-order tree for incremental frequent-pattern mining. Knowledge and Information Systems 11(3), 287–311 (2007)
Li, X., Deng, X., Tang, S.: A fast algorithm for maintenance of association rules in incremental databases. In: Li, X., Zaïane, O.R., Li, Z.-h. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 56–63. Springer, Heidelberg (2006)
Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: Efficient single-pass frequent pattern mining using a prefix-tree. Information Sciences 179, 559–583 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Priya, R.V., Vadivel, A., Thakur, R.S. (2010). Frequent Pattern Mining Using Modified CP-Tree for Knowledge Discovery. In: Cao, L., Feng, Y., Zhong, J. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6440. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17316-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-17316-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17315-8
Online ISBN: 978-3-642-17316-5
eBook Packages: Computer ScienceComputer Science (R0)