Abstract

In Group technology, parts with similar geometry, function, material and process are grouped into part families and the corresponding machines are grouped into machine cells. In cluster analysis, one seeks to find the natural groupings in the data. One searches for patterns in the data set by grouping it into clusters. The goal is to find an optimal grouping for which the data within clusters are similar, but the clusters are dissimilar to each other. Many techniques exist to group the data into clusters. Recently, Support Vector Clustering (SVC) has been employed for cluster analysis. In SVC algorithm, data points are mapped from data space to a high dimensional feature space using a Gaussian kernel. The smallest sphere enclosing in feature space is mapped back to data space where it forms cluster boundary. Thus clusters are formed. The scale parameter of Gaussian kernel and soft margin constant are the two parameters which determine clustering form. A data set of Group technology is considered for investigating performance of the algorithm. Part families are formed by SVC algorithm for the data set within a reasonable time as demonstrated.

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