Selection of 'K' in K-means clustering using GA and VMA
Autor: | Sanjay Chakraborty, Shreya Garg, Subham Raj |
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Rok vydání: | 2019 |
Předmět: | |
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 |
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