Mineração de padrões sequenciais e geração de regras de associação envolvendo temporalidade
Resumo
Data mining aims at extracting useful information from a Database (DB). The mining
process enables, also, to analyze the data (e.g. correlations, predictions, chronological
relationships, etc.). The work described in this document proposes an approach to deal with
temporal knowledge extraction from a DB and describes the implementation of this
approach, as the computational system called S_MEMIS+AR. The system focuses on the
process of finding frequent temporal patterns in a DB and generating temporal association
rules, based on the elements contained in the frequent patterns identified. At the end of the
process performs an analysis of the temporal relationships between time intervals
associated with the elements contained in each pattern using the binary relationships
described by the Allen´s Interval Algebra. Both, the S_MEMISP+AR and the algorithm that
the system implements, were subsidized by the Apriori, the MEMISP and the ARMADA
approaches. Three experiments considering two different approaches were conducted with
the S_MEMISP+AR, using a DB of sale records of products available in a supermarket.
Such experiments were conducted to show that each proposed approach, besides inferring
new knowledge about the data domain and corroborating results that reinforce the implicit
knowledge about the data, also promotes, in a global way, the refinement and extension of
the knowledge about the data.