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Issue No.08 - August (2009 vol.21)
pp: 1147-1161
Jin Soung Yoo , Indiana University-Purdue University, Fort Wayne
Shashi Shekhar , University of Minnesota, Minneapolis
Given a time stamped transaction database and a user-defined reference sequence of interest over time, similarity-profiled temporal association mining discovers all associated item sets whose prevalence variations over time are similar to the reference sequence. The similar temporal association patterns can reveal interesting relationships of data items which co-occur with a particular event over time. Most works in temporal association mining have focused on capturing special temporal regulation patterns such as cyclic patterns and calendar scheme-based patterns. However, our model is flexible in representing interesting temporal patterns using a user-defined reference sequence. The dissimilarity degree of the sequence of support values of an item set to the reference sequence is used to capture how well its temporal prevalence variation matches the reference pattern. By exploiting interesting properties such as an envelope of support time sequence and a lower bounding distance for early pruning candidate item sets, we develop an algorithm for effectively mining similarity-profiled temporal association patterns. We prove the algorithm is correct and complete in the mining results and provide the computational analysis. Experimental results on real data as well as synthetic data show that the proposed algorithm is more efficient than a sequential method using a traditional support-pruning scheme.
Temporal data mining, temporal association pattern, support time sequence, similarity.
Jin Soung Yoo, Shashi Shekhar, "Similarity-Profiled Temporal Association Mining", IEEE Transactions on Knowledge & Data Engineering, vol.21, no. 8, pp. 1147-1161, August 2009, doi:10.1109/TKDE.2008.185
[1] NOAA Economics, climate&file=users business/, 2008.
[2] “After Katrina: Crisis Management, the Only Lifeline Was the Wal-Mart,” FORTUNE Magazine, Oct. 2005.
[3] R. Agarwal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. Int’l Conf. Very Large Databases (VLDB), 1994.
[4] J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” Proc. ACM SIGMOD, 2000.
[5] J. Han and Y. Fu, “Discovery of Multi-Level Association Rules from Large Databases,” Proc. Int’l Conf. Very Large Databases (VLDB), 1995.
[6] J. Park, M. Chen, and P. Yu, “An Effective Hashing-Based Algorithm for Mining Association Rules,” Proc. ACM SIGMOD, 1995.
[7] R. Srikant and R. Agrawal, “Mining Generalized Association Rules,” Proc. Int’l Conf. Very Large Databases (VLDB), 1995.
[8] B. Ozden, S. Ramaswamy, and A. Silberschatz, “Cyclic Association Rules,” Proc. IEEE Int’l Conf. Data Eng. (ICDE), 1998.
[9] Y. Li, P. Ning, X.S. Wang, and S. Jajodia, “Discovering Calendar-Based Temporal Association Rules,” J. Data and Knowledge Eng., vol. 15, no. 2, 2003.
[10] S. Ramaswamy, S. Mahajan, and A. Silberschatz, “On the Discovery of Interesting Patterns in Association Rules,” Proc. Int’lConf. Very Large Databases (VLDB), 1998.
[11], research.html , 2008.
[12] NOAA, El Niño Page, http:/, 2008.
[13] D. Gunopulos and G. Das, “Time Series Similarity Measures and Time Series Indexing,” SIGMOD Record, vol. 30, no. 2, 2001.
[14] C. Bettini, S. Jajodia, and X. Wang, Time Granularities in Databases, Data Mining and Temporal Reasoning. Springer, 2000.
[15] D. Gunopulos and G. Das, “Time Series Similarity Measures,” Tutorial Notes of the ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, 2000.
[16] B. Yi and C. Faloutsos, “Fast Time Sequence Indexing for Arbitrary ${\cal L}_{p}$ Norms,” Proc. Int’l Conf. Very Large Databases (VLDB), 2000.
[17] C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, “Fast Subsequence Matching in Time-Series Database,” Proc. ACM SIGMOD, 1993.
[18] R. Agrawal, C. Faloutsos, and A. Swami, “Efficient Similarity Search in Sequence Databases,” Proc. Int’l Conf. Foundations of Data Organization (FODO), 1993.
[19] E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, “Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases,” J. Knowledge and Information Systems, vol. 3, no. 3, 2001.
[20] R. Agrawal, K.I. Lin, H.S. Sawhney, and K. Shim, “Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Database,” Proc. Int’l Conf. Very Large Databases (VLDB), 1995.
[21] P. Tan, V. Kumar, and J. Srivastava, “Selecting the Right Interestingness Measure for Association Patterns,” Proc. ACM SIGKDD, 2002.
[22] D. Berndt and J. Clifford, “Using Dynamic Time Warping to Find Patterns in Time Series,” Proc. AAAI Workshop Knowledge Discovery in Databases, 1994.
[23] E. Keogh and M. Pazzani, “Scaling Up Dynamic Time Warping for Data Mining Applications,” Proc. ACM SIGKDD, 2000.
[24] N. Yazdani and Z. Ozsoyoglu, “Sequence Matching of Images,” Proc. Int’l Conf. Scientific and Statistical Database Management (SSDBM), 1996.
[25] T. Calders, “Deducing Bounds on the Frequency of Itemsets,” Proc. EDBT Workshop Database Techniques in Data Mining (DTDM), 2002.
[26] Discovery of Changes from the Global Carbon Cycle and Climate System Using Data Mining, nasa-umn /, 2008.
[27] Z. Zheng, R. Kohavi, and L. Mason, “Real World Performance of Association Rule Algorithms,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery in Databases, 2001.
[28] UCI Repository of Machine Learning Databases, , 2008.
[29] G.H. Taylor, “Impacts of El Niño on Southern Oscillation on the Pacific Northwest,” _pnw.html , 2008.
[30] Y. Li, S. Zhu, X.S. Wang, and S. Jajodia, “Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets,” J. Information Sciences, vol. 176, no. 8, 2006.
[31] C. Bettini, X. Wang, S. Jajodia, and J. Lin, “Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences,” IEEE Trans. Knowledge and Data Eng., vol. 10, no. 2, Mar./Apr. 1998.
[32] C. Hidber, “Online Association Rule Mining,” Proc. ACM SIGMOD, 1998.
[33] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. IEEE Int’l Conf. Data Eng. (ICDE), 1995.
[34] G. Dong and J. Li, “Efficient Mining of Emerging Patterns: Discovering Trends and Differences,” Proc. ACM SIGKDD, 1999.
[35] B. Liu, W. Hsu, and Y. Ma, “Discovering the Set of Fundamental Rule Change,” Proc. ACM SIGKDD, 2001.
[36] W. Teng, M. Chen, and P. Yu, “A Regression-Based Temporal Pattern Mining Scheme for Data Streams,” Proc. Int’l Conf. Very Large Databases (VLDB), 2003.
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