In this study, a new center-oriented clustering (CCP) algorithm that provides self-clustering of a network by using a method similar to the center-biased clustering method of the k-means algorithm is presented and differs from the literature in this respect. The proposed algorithm is compared with low-energy adaptive clustering hierarchy (Leach) because it uses similar techniques, as it aims to self-organize irregularly distributed networks. In the experimental study, CCP and Leach algorithms were run in randomly generated network models and the algorithms were compared in terms of the total amount of energy remaining in the network and the number of surviving nodes. The algorithms were run on 15 different wireless sensor network (WSN) models, each of which was irregularly distributed with 100 nodes, and the amount of energy remaining in the network after each trial was recorded and averaged. As a result, it was observed that the energy in the network was 9.4% more efficient in the CCP algorithm. In addition, when the number of surviving nodes was considered, it was observed that an average of 28.3 nodes in the CCP algorithm and 18 nodes in the Leach algorithm survived.