Advertisement

Discovery of Temporal Frequent Patterns Using TFP-Tree

  • Long Jin
  • Yongmi Lee
  • Sungbo Seo
  • Keun Ho Ryu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)

Abstract

Mining temporal frequent patterns in transaction databases, time-series databases, and many other kinds of databases have been widely studied in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and long patterns. In this paper, we propose an efficient temporal frequent pattern mining method using the TFP-tree (Temporal Frequent Pattern tree). This approach has three advantages: (i) one can scan the transaction only once for reducing significantly the I/O cost; (ii) one can store all transactions in leaf nodes but only save the star calendar patterns in the internal nodes. So we can save a large amount of memory. Moreover, we divide the transactions into many partitions by maximum size domain which significantly saves the memory; (iii) we efficiently discover each star calendar pattern in internal node using the frequent calendar patterns of leaf node. Thus we can reduce significantly the computational time. Our performance study shows that the TFP-tree is efficient and scalable for mining, and is about an order of magnitude faster than the classical frequent pattern mining algorithms.

Keywords

Association Rule Leaf Node Internal Node Frequent Pattern Frequent Itemsets 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 1994 Int’l Conf. on Very Large Data Bases, VLDB (1994)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of ICDE 1995 (1995)Google Scholar
  3. 3.
    Brin, S., Motwani, R., Silverstein, C.: Beyond market basket: Generalizing association rules to correlations. In: Proc. of SIGMOD 1998 (1998)Google Scholar
  4. 4.
    Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proc. of the 14th Int’l Conf. on Data Engineering (1998)Google Scholar
  5. 5.
    Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the discovery of interesting patterns in association rules. In: Proc. of the 1998 Int’l Conf. on Very Large Data Bases, VLDB (1998)Google Scholar
  6. 6.
    Li, Y., Ning, P.: Discovering Calendar-based Temporal Association Rules. In: Proc. of the 8th Int’l Symposium on Temporal Representation and Reasoning (2001)Google Scholar
  7. 7.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of 2000 ACM-SIGTMOD Int. Conf. Management of Data, SIGMOD 2000 (2000)Google Scholar
  8. 8.
    Lee, Y.J., Lee, Y.J., Kim, H.K., Hwang, B.H., Ryu, K.H.: Discovering Temporal Relation Rules from Temporal Interval Data. In: EurAsia-ICT 2002 (2002)Google Scholar
  9. 9.
    Seo, S., Jin, L., Lee, J.W., Ryu, K.H.: Similarity pattern discovery using calendar concept hierarchy in time series data. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 565–571. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Long Jin
    • 1
  • Yongmi Lee
    • 1
  • Sungbo Seo
    • 1
  • Keun Ho Ryu
    • 1
  1. 1.Database/Bioinformatics LaboratoryChungbuk National UniversityKorea

Personalised recommendations