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References (cont')

  • Stream and Privacy-Preserving FP Mining
    • A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke. Privacy Preserving Mining of Association Rules. KDD’02.
    • J. Vaidya and C. Clifton. Privacy Preserving Association Rule Mining in Vertically Partitioned Data. KDD’02.
    • G. Manku and R. Motwani. Approximate Frequency Counts over Data Streams. VLDB’02.
    • Y. Chen, G. Dong, J. Han, B. W. Wah, and J. Wang. Multi-Dimensional Regression Analysis of Time-Series Data Streams. VLDB'02.
    • C. Giannella, J. Han, J. Pei, X. Yan and P. S. Yu. Mining Frequent Patterns in Data Streams at Multiple Time Granularities, Next Generation Data Mining:03.
    • A. Evfimievski, J. Gehrke, and R. Srikant. Limiting Privacy Breaches in Privacy Preserving Data Mining. PODS’03.
  • Other Freq. Pattern Mining Applications
    • Y. Huhtala, J. Kärkkäinen, P. Porkka, H. Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE’98.
    • H. V. Jagadish, J. Madar, and R. Ng. Semantic Compression and Pattern Extraction with Fascicles. VLDB'99.
    • T. Dasu, T. Johnson, S. Muthukrishnan, and V. Shkapenyuk. Mining Database Structure; or How to Build a Data Quality Browser. SIGMOD'02.
    • K. Wang, S. Zhou, J. Han. Profit Mining: From Patterns to Actions. EDBT’02.

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