Towards reducing the multidimensionality of OLAP cubes using the
  Evolutionary Algorithms and Factor Analysis Methods
  Data Warehouses are structures with large amount of data collected from
heterogeneous sources to be used in a decision support system. Data Warehouses
analysis identifies hidden patterns initially unexpected which analysis
requires great memory and computation cost. Data reduction methods were
proposed to make this analysis easier. In this paper, we present a hybrid
approach based on Genetic Algorithms (GA) as Evolutionary Algorithms and the
Multiple Correspondence Analysis (MCA) as Analysis Factor Methods to conduct
this reduction. Our approach identifies reduced subset of dimensions from the
initial subset p where p'<p where it is proposed to find the profile fact that
is the closest to reference. GAs identify the possible subsets and the Khi
formula of the ACM evaluates the quality of each subset. The study is based on
a distance measurement between the reference and n facts profile extracted from
the Warehouses.
