Abstract. We implement an algorithm for k-clustering for small k in fixed dimensions and report experimental results here. Although the theoretical bounds on the running time are hopeless for 1 + ε approximating k-clusters, we note that for dimensions 2 and 3, k-clustering is practical for small k ( k ≤ 4 ) and simple enough shapes. For the purposes of this paper, k is a small fixed constant.
DEA is the way of evaluating the performance of decision-making units on the basis of the degree of efficiency. Unfortunately, congestion has been known as a technical inefficiency for at least three decades just because of the lack of determining the congestion border. In this article, we have introduced the concept of the congestion hyperplane without considering the efficiency value. This has considerably reduced the calculation, and the congestion border has been determined. In addition, the existence of this hyperplane is ascertained. For this purpose, we determine the BCC-efficient DMUs. The normal vector of the hyperplane is denoted by these values. This hyperplane can pass from any of the BCC-efficient DMUs. Next, we have shown that the previous congestion methods are covered and improved by this hyperplane.
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