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Mining Frequent Patterns with Convertible Constraints in Large Databases, ICDE Conference, 2001. 1 An Introduction to Frequent Pattern Mining 17 61. J. Pei, J. Han, and W. Wang. Constraint-based Sequential Pattern Mining: The Pattern-Growth Methods, Journal of Intelligent Information Systems, 28(2), pp. 133–160, 2007. 62. P. Shenoy, J. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, D. Shah. Turbo-charging Vertical Mining of Large Databases. ACM SIGMOD Conference, pp. 22–33, 2000. 63. J. Srivastava, R.
Ma. Integrating Classification and Association Rule Mining, ACM KDD Conference, 1998. 53. S. Ma, and J. Hellerstein. Mining Partially Periodic Event Patterns with Unknown Periods, IEEE International Conference on Data Engineering, 2001. 54. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering Frequent Episodes in Sequences, ACM KDD Conference, 1995. 55. R. V. S. Lakshmanan, J. Han, andA. Pang. Exploratory mining and pruning optimizations of constrained associations rules. ACM SIGMOD Conference, 1998.
In the third column, we show the set of items that are frequent in the corresponding transaction for a minimum support value of 3. For example, the item h in transaction with tid value of 2 is an infrequent item with a support value of 2. Therefore, it is not listed in the third column of the corresponding row. Similarly, the pattern {a, b} (or, ab in abbreviated form) is frequent because it has a support value of 3. The frequent patterns are often used to generate association rules. Consider the rule X ⇒ Y , where X and Y are sets of items.