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  • Mining Multi-Level and Quantitative Rules 
    • R. Srikant and R. Agrawal. Mining generalized association rules. VLDB'95.
    • J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. VLDB'95.
    • R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables. SIGMOD'96.
    • T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. SIGMOD'96.
    • K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Computing optimized rectilinear regions for association rules. KDD'97.
    • R.J. Miller and Y. Yang. Association rules over interval data. SIGMOD'97.
    • Y. Aumann and Y. Lindell. A Statistical Theory for Quantitative Association Rules KDD'99.

  • Mining Other Kinds of Rules
    • R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. VLDB'96.
    • B. Lent, A. Swami, and J. Widom. Clustering association rules. ICDE'97.
    • A. Savasere, E. Omiecinski, and S. Navathe. Mining for strong negative associations in a large database of customer transactions. ICDE'98.
    • D. Tsur, J. D. Ullman, S. Abitboul, C. Clifton, R. Motwani, and S. Nestorov. Query flocks: A generalization of association-rule mining. SIGMOD'98.
    • F. Korn, A. Labrinidis, Y. Kotidis, and C. Faloutsos. Ratio rules: A new paradigm for fast, quantifiable data mining. VLDB'98.
    • F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng, “Mining Colossal Frequent Patterns by Core Pattern Fusion”, ICDE'07.

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