Calculating a New Data Mining Algorithm for Market Basket Analysis

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 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.

• Type: Conference
• Year: 2000
• Where: PADL
• Authors: Zhenjiang Hu, Wei-Ngan Chin, Masato Takeichi
• Keywords: Data Mining, Frequent Set, Frequent Set Problem, PADL 2000, Programming Languages