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References (cont')

  • Mining Frequent Patterns in Time-Series Data
    • B. Ozden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. ICDE'98.
    • J. Han, G. Dong and Y. Yin, Efficient Mining of Partial Periodic Patterns in Time Series Database, ICDE'99.
    • H. Lu, L. Feng, and J. Han. Beyond Intra-Transaction Association Analysis: Mining Multi-Dimensional Inter-Transaction Association Rules. TOIS:00.
    • B.-K. Yi, N. Sidiropoulos, T. Johnson, H. V. Jagadish, C. Faloutsos, and A. Biliris. Online Data Mining for Co-Evolving Time Sequences. ICDE'00.
    • W. Wang, J. Yang, R. Muntz. TAR: Temporal Association Rules on Evolving Numerical Attributes. ICDE’01.
    • J. Yang, W. Wang, P. S. Yu. Mining Asynchronous Periodic Patterns in Time Series Data. TKDE’03.

  • FP for Classification and Clustering
    • G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. KDD'99.
    • B. Liu, W. Hsu, Y. Ma. Integrating Classification and Association Rule Mining. KDD’98.
    • W. Li, J. Han, and J. Pei. CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. ICDM'01.
    • H. Wang, W. Wang, J. Yang, and P.S. Yu. Clustering by pattern similarity in large data sets. SIGMOD’ 02.
    • J. Yang and W. Wang. CLUSEQ: efficient and effective sequence clustering. ICDE’03.
    • X. Yin and J. Han. CPAR: Classification based on Predictive Association Rules. SDM'03.
    • H. Cheng, X. Yan, J. Han, and C.-W. Hsu, Discriminative Frequent Pattern Analysis for Effective Classification”, ICDE'07.

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