Abstract
Finding Association Rules is a classical data mining task. The most critical part of Association Rules Mining is about finding the frequent itemsets in the database. Since the introduction of the famouse Apriori algorithm [14], many others have been proposed to find the frequent itemsets. Among all the algorithms, the approach of mining closed itemsets has arisen a lot of interests in data mining community, because the closed itemsets are the condensed representation of all the frequent itemsets. The algorithms taking this approach include TITANIC [8], CLOSET+ [6], DCI-Closed [4], FCI-Stream [3], GC-Tree [15], etc. While the above algorithms are trying to improve the performance of finding the Intents of Formal Concepts (in anther word, the closed itemsets), they missed another important information: the Extents of Formal Concepts. In this paper, we propose an online algorithm, TGC-Tree, which is adapted from the GC-Tree algorithm [15], that could be used to trace the closed itemsets(Intents) and the corresponding transaction sets(Extents) simultaneously in an incremental way.
This is a preview of subscription content, log in via an institution to check access.
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
Ben Yahia, S., Hamrouni, T., Mephu Nguifo, E.: Frequent closed itemset based algorithms: a thorough structural and analytical survey. ACM SIGKDD Explorations Newsletter 8(1), 93–104 (2006)
Lin, C.-H., Chiu, D.-Y., Wu, Y.-H., Chen, A.L.P.: Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window. In: Proc. of SDM Conf. (2005)
Jiang, N., Gruenwald, L.: CFI-Stream: mining closed frequent itemsets in data streams. In: Proc. of KDD Conf., pp. 592–597 (2006)
Lucchese, C., Orlando, S., Perego, R.: DCI Closed: A Fast and Memory Efficient Algorithm to Mine Frequent Closed Itemsets. In: Proc. of FIMI Conf. (2004)
Lucchese, C., Orlando, S., Perego, R.: Fast and Memory Efficient Mining of Frequent Closed Itemsets. IEEE Journal Transactions of Knowledge and Data Engineering (TKDE) 18(1), 21–36 (2006)
Wang, J., Han, J., Pei, J.: CLOSET+: Searching for the Best Strategies for Mining Frequent Closed Itemsets. In: Proc. of KDD Conf. (2003)
Zaki, M.J., Hsiao, C.-J.: CHARM: An Efficient algorithm for closed itemsets mining. In: Proc. of SIAM ICDM Conf. (2002)
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with TITANIC. Journal of Knowledge and Data Engineering (KDE) 2(42), 189–222 (2002)
Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. Technical Report 01-1, Computer Science Dept., Rensselaer Polytechnic Institute (March 2001)
Lucchese, C., Orlando, S., Palmerini, P., Perego, R., Silvestri, F.: KDCI: a multistrategy algorithm for mining frequent sets. In: Proc. of ICDM Conf. (2003)
Orlando, S., Palmerini, P., Perego, R., Silvestri, F.: Adaptive and resource-aware mining of frequent sets. Proc. of ICDM Conf. (2002)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering Frequent Closed Itemsets for Association Rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)
Pei, J., Han, J., Mao, R.: CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Proc. of DMKD Conf. (May 2000)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Int’l Conf. on Very Large Databases (1994)
Chen, J., Li, S.: GC-Tree: A Fast Online Algorithm for Mining Frequent Closed Itemsets. In: Proc. of HPDMA, PAKDD Conf. (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, J., Zhou, B. (2008). TGC-Tree: An Online Algorithm Tracing Closed Itemset and Transaction Set Simultaneously. In: Tokunaga, T., Ortega, A. (eds) Large-Scale Knowledge Resources. Construction and Application. LKR 2008. Lecture Notes in Computer Science(), vol 4938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78159-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-78159-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78158-5
Online ISBN: 978-3-540-78159-2
eBook Packages: Computer ScienceComputer Science (R0)