Construct · 2 fields · 3 citations
One name. Different field. Different construct. One map.
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In data mining, market basket analysis is the discovery of association rules among items that tend to appear together in the same transaction, so a large log of purchase records yields patterns of the form 'if these items, then that item' scored by support, confidence, and lift. Agrawal, Imieliński, and Swami's 1993 Mining association rules between sets of items in large databases introduced the problem, and Agrawal and Srikant's 1994 Apriori algorithm made the search tractable by pruning infrequent item sets. The referent is a computational pattern extracted from transaction data, the origin of the folkloric 'beer and diapers' co-purchase, not a bundle of goods priced over time, a tradable portfolio, or a shopper's trip.[1][2]
In consumer behavior, the shopping basket is a behavioral construct: the set of categories a household chooses to buy on a single store trip, whose composition reflects complementarity, coincident needs, and shared marketing sensitivity across categories. Manchanda, Ansari, and Gupta's 1999 The 'shopping basket': A model for multicategory purchase incidence decisions modeled why categories are bought together in one basket, separating genuine complementarity from co-incidence in demand. The referent is an observed act of shopping by a real household on a real trip, not an algorithm's mined rule, a macro price bundle, or a financial portfolio, even though the co-purchase it captures is what basket-analysis algorithms later mine.[3]
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