A model-based algorithm for quantity and parameters of clusters discovery

• Main Authors: Jiang, Kunpeng, Yang, Kun, Qu, Haipeng, Li, Miao, Wang, Shijun
• Format: Conference Proceeding
• Language: English
• Published: SPIE 03.02.2023
• ISBN: 9781510661363; 1510661360
• ISSN: 0277-786X
• DOI: 10.1117/12.2660137

It is called unsupervised learning to solve various problems in pattern recognition based on training samples with unknown categories (unlabeled). Clustering algorithm is a kind of unsupervised learning algorithm. Although a lot of clustering algorithms have been studied in modern science and applied in many fields, it is their common problem that the quantity of clusters has to be given. This paper proposes a model-based algorithm for quantity and parameters of clusters discovery (QPCD) which can calculate the quantity and parameters of clusters according to the characteristics of the data themselves. The algorithm initially fills the shortage of existing clustering algorithms. The paper proposes an elementary judgment rule on whether the cluster center is appropriate. According to the elementary judgment rule, the algorithm proposed by the paper can calculate the correct quantity of clusters, and give the corresponding clustering parameters according to the data characteristics. Monte Carlo simulation is used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in the paper can start with an arbitrary given cluster center and get the cluster centers close to the actual cluster centers of the data themselves, so as to complete the clustering unsupervised.