Principal component analysis and self organizing map for visual clustering of machine-part cell formation in cellular manufacturing system

TL;DR

This paper combines principal component analysis and self-organizing maps to improve visual clustering and data extraction for machine-part cell formation in manufacturing systems, addressing high-dimensional data challenges.

Contribution

It introduces a novel visualization methodology using PCA and SOM, enhancing cluster detection in large, sequence-based cell formation problems.

Findings

PCA explains most data variance but struggles with large sequence data.

SOM with color mapping improves cluster visualization.

The method significantly advances cellular manufacturing research.

Abstract

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.