Algorithms for hierarchical clustering: an overview, II

By Fionn Murtagh, Pedro Contreras. WIREs Data Mining Knowl. Discov., 2017

348 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ae9ef8cedddd3acf1a77467eb4c1e0d719d98350

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. Xiangwen Wang
2. Zhenyu Lu
3. Yue Xi
4. Yonggang Lu
5. Xingcheng Ran

In Information Systems

1. Xiangwen Wang
2. Zhenyu Lu
3. Yue Xi
4. Yonggang Lu
5. Xingcheng Ran

In Signal Processing

1. Xiangwen Wang
2. Zhenyu Lu
3. Yue Xi
4. Yonggang Lu
5. Xingcheng Ran

Citations in the corpus

• Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, background): “The dissimilarity D of a dataset X , X = { x 1 , ⋯ , x n } , is defined as the function that need meet the following conditions (Xu and Wunsch II 2005; Murtagh and Contreras 2012, 2017): (1) Symmetric, D ( x i , x j ) = D ( x j , x i ) ; (2) Positivity, D ( x i , x j ) ≥ 0 for all x i and x j ; (3)…”