  The present paper attempts to generate visual clustering and data extraction
of cell formation problem using both principal component analysis (PCA) and
self organizing map (SOM) from input of sequence based machine-part incidence
matrix. First, the focus is to utilize PCA for extracting high dimensionality
of input variables and project the dataset onto a 2-D space. Second, the
unsupervised competitive learning of SOM algorithm is used for data
visualization and subsequently, to solve cell formation problem based on
ordinal sequence data via the node cluster on the SOM map. Although the
numerically illustrated results from dataset revealed that PCA has explained
most of the cumulative variance of data but in reality when the very large
dimensional cell formation problem based on sequence is available then to
obtain the clustering structure from PCA projection is become very difficult.
Most importantly, in the visual clustering of ordinal data, the use of U-matrix
alone can not be efficient to get the cluster structure but with color
extraction, hit map, labeling via the SOM node map it becomes a powerful
clustering visualization methodology and thus the present research contribute
significantly in the research of cellular manufacturing.
