The promotion and accumulation strategies in transformational programming
ACM Transactions on Programming Languages and Systems (TOPLAS) - Lecture notes in computer science Vol. 174
Safe fusion of functional expressions
LFP '92 Proceedings of the 1992 ACM conference on LISP and functional programming
FPCA '93 Proceedings of the conference on Functional programming languages and computer architecture
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A transformation method for dynamic-sized tabulation
Acta Informatica
Shortcut deforestation in calculational form
FPCA '95 Proceedings of the seventh international conference on Functional programming languages and computer architecture
Algebra of programming
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Parallelization in calculational forms
POPL '98 Proceedings of the 25th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
New Generation Computing
Tabulation Techniques for Recursive Programs
ACM Computing Surveys (CSUR)
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
A Compositional Framework for Mining Longest Ranges
DS '02 Proceedings of the 5th International Conference on Discovery Science
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The general goal of data mining is to extract interesting correlated information from large collection of data. A key computationally-intensive subproblem of data mining involves finding frequent sets in order to help mine association rules for market basket analysis. Given a bag of sets and a probability, the frequent set problem is to determine which subsets occur in the bag with some minimum probability. This paper provides a convincing application of program calculation in the derivation of a completely new and fast algorithm for this practical problem. Beginning with a simple but inefficient specification expressed in a functional language, the new algorithm is calculated in a systematic manner from the specification by applying a sequence of known calculation techniques.