Calculating a New Data Mining Algorithm for Market Basket Analysis
Zhenjiang Hu, Wei-Ngan Chin, Masato Takeichi · 2000
. The general goal of data mining is to extract interesting correlated information from large collection of data. A key computationallyintensive 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. 1 Introduction Program derivation has enjoyed considerable interests over the past two decades. Early work concentrated on deriving programs in imperative languages, such as Dijkstra's Guarded Comman...