Research on K-Value Selection Method of K-Means Clustering Algorithm

By Chunhui Yuan, Haitao Yang. ET Journal, 2019

810 citations recorded by the source corpus.

Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, the K-value of clustering needs to be given in advance and the choice of K-value directly affect the convergence result. To solve this problem, we mainly analyze four K-value selection algorithms, namely Elbow Method, Gap Statistic, Silhouette Coefficient, and Canopy; give the pseudo code of the algorithm; and use the standard data set Iris for experimental verification. Finally, the verification results are evaluated, the advantages and disadvantages of the above four algorithms in a K-value selection are given, and the clustering range of the data set is pointed out.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4f76785d62eb0f6eb21ee71280f446e61a5c4aac

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Amina Bolatkan
2. Kazuma Kobayashi
3. Jun Sese
4. Ryuji Hamamoto

In Information Systems

1. Amina Bolatkan
2. Kazuma Kobayashi
3. Ryuji Hamamoto

In Signal Processing

1. Amina Bolatkan
2. Kazuma Kobayashi
3. Ryuji Hamamoto

Citations in the corpus

• Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data (2020, methodology): “To obtain the appropriate number of clusters, elbow-method was first used [16].”