Identifying the number of clusters for K-Means: A hypersphere density based approach

Autor:	Nanjundan, Sukavanan, Sankaran, Shreeviknesh, Arjun, C. R., Anand, G. Paavai
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Application of K-Means algorithm is restricted by the fact that the number of clusters should be known beforehand. Previously suggested methods to solve this problem are either ad hoc or require parametric assumptions and complicated calculations. The proposed method aims to solve this conundrum by considering cluster hypersphere density as the factor to determine the number of clusters in the given dataset. The density is calculated by assuming a hypersphere around the cluster centroid for n-different number of clusters. The calculated values are plotted against their corresponding number of clusters and then the optimum number of clusters is obtained after assaying the elbow region of the graph. The method is simple, easy to comprehend, and provides robust and reliable results. Comment: 5 pages, 13 figures, International Conference on Computers, Communication and Signal Processing - 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.00643 Zobrazit plný text záznamu View this record from Arxiv