The success of any product in today’s competitive market is dictated by its ability to satisfy the needs of the customers. In this effort, it is important to group similar needs to recognize representative needs, and then identify product requirements that can fulfill these representative needs. One approach to this is to apply Subjective Clustering (SC) to sample data (grouping of customer needs by a sample of customers); however, clusters obtained by SC give only a point estimate of the primary clusters of customer needs by the entire population of customers (population primary clusters). Applying Bootstrap to SC (BS-SC) helps engineers to make inferences on the population primary clusters. In this paper, we randomly pulled out samples of different sizes from both the simulation approach using simulation-generated population data and the empirical approach using experimental population data, and compared the accuracies of SC and BS-SC. Regardless of population sizes, when the sample size was small, BS-SC was more accurate than SC in estimating the population primary clusters. Also, the BS-SC and SC estimates were similar for both simulation and empirical approaches.

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