Resumo

NARANJO CUERVO, Roberto Carlos  e  SIERRA MARTINEZ, Luz Marina. Software tool for analysing the family shopping basket without candidate generation. Ing. Investig. [online]. 2009, vol.29, n.1, pp.60-68. ISSN 0120-5609.

Tools leading to useful knowledge being obtained for supporting marketing decisions being taken are currently needed in the ecommerce environment. A process is needed for this which uses a series of techniques for data-processing; data-mining is one such technique enabling automatic information discovery. This work presents the association rules as a suitable technique for discovering how customers buy from a company offering business to consumer (B2C) e-business, aimed at supporting decision-making in supplying its customers or capturing new ones. Many algorithms such as A priori, DHP, Partition, FP-Growth and Eclat are available for implementing association rules; the following criteria were defined for selecting the appropriate algorithm: database insert, computational cost, performance and execution time. The development of a software tool is also presented which involved the CRISP-DM approach; this software tool was formed by the following four sub-modules: data pre-processing, data-mining, results analysis and results application. The application design used three-layer architecture: presentation logic, business logic and service logic. Data warehouse design and algorithm design were included in developing this data-mining software tool. It was tested by using a FoodMart company database; the tests included performance, functionality and results’ validity, thereby allowing association rules to be found. The results led to concluding that using association rules as a data mining technique facilitates analysing volumes of information for B2C e-business services which represents a competitive advantage for those companies using Internet as their sales’ media.

Palavras-chave : data-mining; B2C e-business; family shopping basket analysis.

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