Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
| Autor: | Ishwar Baidari, Channamma Patil |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
lcsh:T58.5-58.64
Computer science lcsh:Information technology Optimal value k Computational Mechanics Value (computer science) 02 engineering and technology Data depth Average depth computer.software_genre Depth between cluster lcsh:QA75.5-76.95 Computer Science Applications Depth difference 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Depth within cluster Data mining lcsh:Electronic computers. Computer science Cluster analysis computer |
| Zdroj: | Data Science and Engineering, Vol 4, Iss 2, Pp 132-140 (2019) |
| ISSN: | 2364-1541 2364-1185 |
| DOI: | 10.1007/s41019-019-0091-y |
| Popis: | This paper proposes a new method called depth difference (DeD), for estimating the optimal number of clusters (k) in a dataset based on data depth. The DeD method estimates the k parameter before actual clustering is constructed. We define the depth within clusters, depth between clusters, and depth difference to finalize the optimal value of k, which is an input value for the clustering algorithm. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed DeD method outperforms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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