Abstract:
Understanding the information and clusters hidden
inside multidimensional data can be challenging and
complicated. Dimension reduction is usually considered as the
first step for data analysis and interpretation. The focus of
this paper is on the improvement of data clustering
performance of Self Organising Maps (SOM) by embedding
Auto-Associative Neural Networks (AANN). SOM is known as
a computational tool that carries out topology preservation
from high-dimensional input space onto a low-dimensional
grid such as two-dimensional (2D) map. It has been used to
visualize and explore inherent clusters and properties of the
data. In this paper, a structurally flexible combination of
AANN and SOM is developed, applied and investigated on
Iris Flowers and Italian Olive oils datasets. The results have
shown that the combined technique of AANNSOM has led to
improvement of data clustering performance. It has reduced
quantization error by 93.1%, and topographic error by
35.2%, when compared to SOM alone.
