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A Novel Optimal Clustering Model With Neutrosophic Clustering

In recent years, data clustering, as one of the main tasks of unsupervised learning, plays an important role in data mining. In order to consider both geometric shape and spatial structure of data points, a novel clustering model with neutrosophic clustering is proposed. Moreover, considering the impact of different attributes of data on clustering results, the given clustering model obtains attribute weight through automatic iteration. To describe the model, a suitable objection function is built, and the clustering problem is transformed into a constrained minimization problem and solved by the Lagrange multiplier method. Finally, the experimental results of artificial data and real data show that the algorithm is feasible.