Human resource performance clustering by using self regulating clustering method

Abstract

In organizational performance evaluation, performance of each staff plays a key role for organization. Although, the whole is greater than the sum of its parts, outstanding personnel performances determine the performance of the whole organization. At this point, an understanding and awareness of individual differences in performance stands as a critical point in making decisions related to promotion, wage determination, fringe benefit allotment and etc. since, those decisions are directly related to personnel motivation, retention and further organizational performance. Data mining and clustering methods can be used in personnel performance evaluation. After gathering personnel performance data from human resource department, the need to take some specific results about performance measurement and evaluation may be addressed by clustering methods. By clustering, a distinction between personnel by grouping them by their performance grades both assists attaining a bird’s eye view of the general performance of the organization and each staff’s contribution level to the organizational performance. For evaluating cluster numbers using x-mean algorithm, the algorithm finds optimum cluster number for cluster distribution. Hence, our problem of an optimum clustering schema for personnel performance data may be addressed. These results show the usefulness of an innovative technique when applied to research so far conducted through traditional methodologies, and brings to the surface questions about the universal applicability of the widely accepted relationship between superior HRM and superior business performance.