Author(s)

Mengyao Cui ¹

Affiliation(s)

¹ Shandong University of Finance and Economics, Jinan, Shandong, China

Corresponding Author

ABSTRACT

The K-means clustering algorithm is a commonly used algorithm in the financial field, and it is also an unsupervised learning algorithm. It is characterized as an easy and simple algorithm and is widely used in the fields such as machine learning and stock trading. However, the K-means algorithm also has certain shortcomings. The k value is difficult to determine, and the initial center of the cluster is difficult to find. This article introduces the idea of K-means algorithm, using the elbow method to find the most suitable k value.

KEYWORDS

CITE THIS PAPER

Mengyao Cui, Introduction to the K-Means Clustering Algorithm Based on the Elbow Method. Geoscience and Remote Sensing (2020) Vol. 3: 9-16. DOI: http://dx.doi.org/10.23977/geors.2020.030102.

REFERENCES

[1] Dubey A,Choubey A.A systematic review on k-means clustering techniques[J].International Journal of Scientific Research Engineering & Technology,2017,6(6):624-627.
[2] Saroj,Kavita.Review:study on simple k mean and modified K mean clustering technique[J].International Journal of Computer Science Engineering and Technology,2016,6(7):279-281.
[3] Anil K.Jain, Data clustering: 50years beyond k-means, 2009, pp.651-666.
[4] Jain A K,Murty M N,Flynn P J.Data clustering:a review[J].ACM Computing Surveys,1999,3(3):264-323.
[5] Alexander L,Jiang S,Murga M,et al.Origin-destination trips by purpose and time of day inferred from mobile phone data[J].Transportation Research:Part C Emerging Technologies,2015,58:240-250.

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