Approaches for finding Optimal Number of Clusters using K-Means and Agglomerative Hierarchical Clustering Techniques-Reference-Cited by-同舟云学术

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Approaches for finding Optimal Number of Clusters using K-Means and Agglomerative Hierarchical Clustering Techniques

Published:2022-07-21 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)
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Author:

Patel Punyaban¹,Sivaiah Borra²,Patel Riyam³

Affiliation:

1. CMR Technical Campus,Department of Computer Science and Engineering,Hyderabad,India

2. CMR College of Engineering & Technology,Department of Computer Science and Engineering,Hyderabad,India

3. SRM Institute of Science & Technology, SRM University,Department of Computer Science and Engineering (AI & ML),Chennai,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9862308/9862027/09862439.pdf?arnumber=9862439

Reference22 articles.

1. An Empirical Evaluation of k-Means Clustering Technique and Comparison

2. Estimating the number of clusters in a data set via the gap statistic

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

4. The Silhouette Width Criterion for Clustering and Association Mining to Select Image Features

5. An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index

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