A Hybrid of t-Distributed Stochastic Neighbors Embadding and Markov Cluster in Cluster Analysis
Keywords:
Cluster analysis, t-Distributed Stochastic Neighbors Embadding, Markov clusterAbstract
This research investigates the performance of the hybrid t-Distributed Stochastic Neighbor Embedding (t-SNE) and Markov Clustering (MCL) method in reducing dimensionality of data and performing clustering analysis. The iris dataset was used to evaluate the performance of the method. The results showed that the hybrid t-SNE and MCL method produced well-defined clusters with good separations between clusters, as indicated by a Silhouette score of 0.682. The Calinski-Harabasz (CH) Index and the Davies-Bouldin (D-B) Index were 330.226 and 0.46056, respectively, showing that the method was able to produce accurate clusters with low similarities between clusters. The reduction of the iris dataset into two dimensions using t-SNE was also effective in capturing the relationships between data points. Overall, the results of this research demonstrate the potential of the hybrid t-SNE and MCL method as a promising approach for clustering analysis.
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Copyright (c) 2023 Bibit Waluyo Aji, Bambang Irawanto
This work is licensed under a Creative Commons Attribution 4.0 International License.
