Silhouette (clustering) (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Silhouette (clustering)" in English language version.

refsWebsite

Global rank English rank

9doi.org

2^nd place

4arxiv.org

69^th place

59^th place

3worldcat.org

5^th place

3semanticscholar.org

11^th place

8^th place

1archive.org

6^th place

1ieee.org

652^nd place

515^th place

1tandfonline.com

507^th place

429^th place

1springer.com

274^th place

309^th place

1elsevier.com

610^th place

704^th place

1sciencedirect.com

149^th place

178^th place

archive.org

Leonard Kaufman; Peter J. Rousseeuw (1990). Finding groups in data : An introduction to cluster analysis. Hoboken, NJ: Wiley-Interscience. p. 87. doi:10.1002/9780470316801. ISBN 9780471878766.

arxiv.org

R.C. de Amorim, C. Hennig (2015). "Recovering the number of clusters in data sets with noise features using feature rescaling factors". Information Sciences. 324: 126–145. arXiv:1602.06989. doi:10.1016/j.ins.2015.06.039. S2CID 315803.
Lenssen, Lars; Schubert, Erich (2022). Clustering by Direct Optimization of the Medoid Silhouette. International Conference on Similarity Search and Applications. pp. 190–204. arXiv:2209.12553. doi:10.1007/978-3-031-17849-8_15. Retrieved 2022-10-20.
Batool, Fatima; Hennig, Christian (2021). "Clustering with the Average Silhouette Width". Computational Statistics & Data Analysis. 158: 107190. arXiv:1910.11339. doi:10.1016/j.csda.2021.107190. S2CID 219260336.
Lenssen, Lars; Schubert, Erich (2024-02-01). "Medoid Silhouette clustering with automatic cluster number selection". Information Systems. 120: 102290. arXiv:2309.03751. doi:10.1016/j.is.2023.102290. ISSN 0306-4379.

doi.org

Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis". Computational and Applied Mathematics. 20: 53–65. doi:10.1016/0377-0427(87)90125-7.
Monshizadeh, Mehrnoosh; Khatri, Vikramajeet; Kantola, Raimo; Yan, Zheng (2022-11-01). "A deep density based and self-determining clustering approach to label unknown traffic". Journal of Network and Computer Applications. 207: 103513. doi:10.1016/j.jnca.2022.103513. ISSN 1084-8045. However, both measures [silhouette coefficient and edge correlation] prefer convex-shaped clusters and cannot adapt to all cluster shapes produced by DBSCAN.
R.C. de Amorim, C. Hennig (2015). "Recovering the number of clusters in data sets with noise features using feature rescaling factors". Information Sciences. 324: 126–145. arXiv:1602.06989. doi:10.1016/j.ins.2015.06.039. S2CID 315803.
Leonard Kaufman; Peter J. Rousseeuw (1990). Finding groups in data : An introduction to cluster analysis. Hoboken, NJ: Wiley-Interscience. p. 87. doi:10.1002/9780470316801. ISBN 9780471878766.
Hruschka, E.R.; de Castro, L.N.; Campello, R.J.G.B. (2004). Evolutionary Algorithms for Clustering Gene-Expression Data. Fourth IEEE International Conference on Data Mining (ICDM'04). IEEE. pp. 403–406. doi:10.1109/ICDM.2004.10073.
Van der Laan, Mark; Pollard, Katherine; Bryan, Jennifer (2003). "A new partitioning around medoids algorithm". Journal of Statistical Computation and Simulation. 73 (8): 575–584. doi:10.1080/0094965031000136012. ISSN 0094-9655. S2CID 17437463.
Lenssen, Lars; Schubert, Erich (2022). Clustering by Direct Optimization of the Medoid Silhouette. International Conference on Similarity Search and Applications. pp. 190–204. arXiv:2209.12553. doi:10.1007/978-3-031-17849-8_15. Retrieved 2022-10-20.
Batool, Fatima; Hennig, Christian (2021). "Clustering with the Average Silhouette Width". Computational Statistics & Data Analysis. 158: 107190. arXiv:1910.11339. doi:10.1016/j.csda.2021.107190. S2CID 219260336.
Lenssen, Lars; Schubert, Erich (2024-02-01). "Medoid Silhouette clustering with automatic cluster number selection". Information Systems. 120: 102290. arXiv:2309.03751. doi:10.1016/j.is.2023.102290. ISSN 0306-4379.

elsevier.com

linkinghub.elsevier.com

Batool, Fatima; Hennig, Christian (2021). "Clustering with the Average Silhouette Width". Computational Statistics & Data Analysis. 158: 107190. arXiv:1910.11339. doi:10.1016/j.csda.2021.107190. S2CID 219260336.

ieee.org

ieeexplore.ieee.org

Hruschka, E.R.; de Castro, L.N.; Campello, R.J.G.B. (2004). Evolutionary Algorithms for Clustering Gene-Expression Data. Fourth IEEE International Conference on Data Mining (ICDM'04). IEEE. pp. 403–406. doi:10.1109/ICDM.2004.10073.

sciencedirect.com

Lenssen, Lars; Schubert, Erich (2024-02-01). "Medoid Silhouette clustering with automatic cluster number selection". Information Systems. 120: 102290. arXiv:2309.03751. doi:10.1016/j.is.2023.102290. ISSN 0306-4379.

semanticscholar.org

api.semanticscholar.org

R.C. de Amorim, C. Hennig (2015). "Recovering the number of clusters in data sets with noise features using feature rescaling factors". Information Sciences. 324: 126–145. arXiv:1602.06989. doi:10.1016/j.ins.2015.06.039. S2CID 315803.
Van der Laan, Mark; Pollard, Katherine; Bryan, Jennifer (2003). "A new partitioning around medoids algorithm". Journal of Statistical Computation and Simulation. 73 (8): 575–584. doi:10.1080/0094965031000136012. ISSN 0094-9655. S2CID 17437463.
Batool, Fatima; Hennig, Christian (2021). "Clustering with the Average Silhouette Width". Computational Statistics & Data Analysis. 158: 107190. arXiv:1910.11339. doi:10.1016/j.csda.2021.107190. S2CID 219260336.

springer.com

link.springer.com

Lenssen, Lars; Schubert, Erich (2022). Clustering by Direct Optimization of the Medoid Silhouette. International Conference on Similarity Search and Applications. pp. 190–204. arXiv:2209.12553. doi:10.1007/978-3-031-17849-8_15. Retrieved 2022-10-20.

tandfonline.com

Van der Laan, Mark; Pollard, Katherine; Bryan, Jennifer (2003). "A new partitioning around medoids algorithm". Journal of Statistical Computation and Simulation. 73 (8): 575–584. doi:10.1080/0094965031000136012. ISSN 0094-9655. S2CID 17437463.

worldcat.org

search.worldcat.org

Monshizadeh, Mehrnoosh; Khatri, Vikramajeet; Kantola, Raimo; Yan, Zheng (2022-11-01). "A deep density based and self-determining clustering approach to label unknown traffic". Journal of Network and Computer Applications. 207: 103513. doi:10.1016/j.jnca.2022.103513. ISSN 1084-8045. However, both measures [silhouette coefficient and edge correlation] prefer convex-shaped clusters and cannot adapt to all cluster shapes produced by DBSCAN.
Van der Laan, Mark; Pollard, Katherine; Bryan, Jennifer (2003). "A new partitioning around medoids algorithm". Journal of Statistical Computation and Simulation. 73 (8): 575–584. doi:10.1080/0094965031000136012. ISSN 0094-9655. S2CID 17437463.
Lenssen, Lars; Schubert, Erich (2024-02-01). "Medoid Silhouette clustering with automatic cluster number selection". Information Systems. 120: 102290. arXiv:2309.03751. doi:10.1016/j.is.2023.102290. ISSN 0306-4379.