An Empirical Study of Requirements Specification Using Z and UML

SSDM: A Semantically Similar Data Mining Algorithm

Luiz Eduardo Galvão Martins

Eduardo L. G. EscovarMauro BiajizMarina T. Pires Vieira

This article presents an empirical study that used some UML diagrams (semi-formal modeling) and Z language (formal modeling) to make a requirements specification of an information system. The empirical study could show the benefits of formal modeling for the quality improvement of the requirements specification of an information system. With the experiment could also be perceived the necessity of a clear correspondence between the modeling approaches. Such correspondence was established during the experiment, allowing that the specification could be correctly articulated. Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. In this paper we present a new algorithm called SSDM (Semantically Similar Data Miner), which considers not only exact matches between items, but also the semantic similarity between them. SSDM uses fuzzy logic concepts to represent the similarity degree between items, and proposes a new way of obtaining support and confidence for the association rules containing these items.

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