Algoritmo SSDM para a mineração de dados semanticamente similares.
Abstract
The SSDM algorithm, created to allow semantically similar data mining, is presented in this
work. Using fuzzy logic concepts, this algorithm analyzes the similarity grade between items,
considering it if it is greater than a user-defined parameter. When this occurs, fuzzy associations
between items are established, and are expressed in the association rules obtained. Therefore,
besides associations discovered by conventional algorithms, SSDM also discovers semantic
associations, showing them together with the other rules obtained. To do that, strategies are
defined to discover these associations and calculate the support and the confidence of the rules where they appear.