Selection of 'K' in K-means clustering using GA and VMA

Autor:	Sanjay Chakraborty, Shreya Garg, Subham Raj
Rok vydání:	2019
Předmět:	Local optimum Computer science Genetic algorithm Metric (mathematics) k-means clustering Cluster (physics) Data mining Cluster analysis computer.software_genre Measure (mathematics) computer Selection (genetic algorithm)
Zdroj:	International Journal of Data Science. 4:63
ISSN:	2053-082X 2053-0811
Popis:	The K-means algorithm is the most widely used partitional clustering algorithms. In spite of several advances in K-means clustering algorithm, it suffers in some drawbacks like initial cluster centres, stuck in local optima etc. The initial guessing of cluster centres lead to the bad clustering results in K-means and this is one of the major drawbacks of K-means algorithm. In this paper, a new strategy is proposed where we have blended K-means algorithm with genetic algorithm (GA) and volume metric algorithm (VMA) to predict the best value of initial cluster centres, which is not in the case of only K-means algorithm. The paper concludes with the analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different well-known datasets from UCI machine learning repository.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4aaa6e1451f1e6e6106c978d1b6c9326 https://doi.org/10.1504/ijds.2019.098361 Zobrazit plný text záznamu