Mineração multi-relacional: o algoritmo GFP-growth
Resumo
Data mining is the phase of the knowledge discovery in database process where an
algorithm is applied to the available data, in order to prove a hypothesis or discover a still
unknown pattern. The traditional data mining techniques can deal only with single tables;
however it is interesting to look for patterns involving several related tables, aiming to
analyze the existing relation between the entities present in one table and the data of the same
entities present in another table.
Depending on the relationship existing between these tables, applying a traditional
algorithm to the joint table is not sufficient, as the joint table may contain duplicated attribute
values which interfere in the analysis process of the generated rules.
In order to solve this problem, this project adopts an approach which consists on
looking for association rules mining the joint table. The adopted process considers the groups
of tuples, where each group is formed by tuples of the same entity.
Following this approach the GFP-Growth algorithm was developed, which is presented
in this monograph along with its results and comparisons with other multi-relational
algorithms.