Silhouettes

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

This software is also peer reviewed by journal TOMS.

Keywords for this software

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References in zbMATH (referenced in 166 articles , 1 standard article )

Showing results 1 to 20 of 166.
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  1. Afridi, Mohammad Khan; Azam, Nouman; Yao, JingTao: Variance based three-way clustering approaches for handling overlapping clustering (2020)
  2. Alonso, Andrés M.; Galeano, Pedro; Peña, Daniel: A robust procedure to build dynamic factor models with cluster structure (2020)
  3. Fordellone, Mario; Vichi, Maurizio: Finding groups in structural equation modeling through the partial least squares algorithm (2020)
  4. Guillaume, Serge; Ros, Frédéric: A family of unsupervised sampling algorithms (2020)
  5. Kim, Joonpyo; Oh, Hee-Seok: Pseudo-quantile functional data clustering (2020)
  6. Smallman, Luke; Underwood, William; Artemiou, Andreas: Simple Poisson PCA: an algorithm for (sparse) feature extraction with simultaneous dimension determination (2020)
  7. Tang, Ming; Liao, Huchang; Xu, Jiuping; Streimikiene, Dalia; Zheng, Xiaosong: Adaptive consensus reaching process with hybrid strategies for large-scale group decision making (2020)
  8. Tavares, Ana Helena; Raymaekers, Jakob; Rousseeuw, Peter J.; Brito, Paula; Afreixo, Vera: Clustering genomic words in human DNA using peaks and trends of distributions (2020)
  9. Wohlgemuth, Murilo; Fries, Carlos Ernani; Sant’Anna, Ângelo Márcio Oliveira; Giglio, Ricardo; Fettermann, Diego Castro: Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression (2020)
  10. Yu, Jaehong; Zhong, Hua; Kim, Seoung Bum: An ensemble feature ranking algorithm for clustering analysis (2020)
  11. Alexandrov, Boian S.; Stanev, Valentin G.; Vesselinov, Velimir V.; Rasmussen, Kim Ø.: Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers (2019)
  12. Alonso, Andrés M.; Peña, Daniel: Clustering time series by linear dependency (2019)
  13. Hearne, Gareth M.; Milne, Andrew J.; Dean, Roger T.: Distributional analysis of (n)-dimensional feature space for 7-note scales in 22-TET (2019)
  14. He, Zhenfeng; Yu, Chunyan: Clustering stability-based evolutionary K-means (2019)
  15. Iglesias, Félix; Zseby, Tanja; Ferreira, Daniel; Zimek, Arthur: MDCGen: multidimensional dataset generator for clustering (2019)
  16. Le Brigant, Alice; Puechmorel, Stéphane: Quantization and clustering on Riemannian manifolds with an application to air traffic analysis (2019)
  17. López de Prado, Marcos; Lewis, Michael J.: Detection of false investment strategies using unsupervised learning methods (2019)
  18. Maragoudakis, Manolis: Data analysis, simulation and visualization for environmentally safe maritime data (2019)
  19. Pham-Toan, D.; Vo-Van, T.; Pham-Chau, A. T.; Nguyen-Trang, T.; Ho-Kieu, D.: A new binary adaptive elitist differential evolution based automatic (k)-medoids clustering for probability density functions (2019)
  20. Pulina, Luca; Seidl, Martina: The 2016 and 2017 QBF solvers evaluations (QBFEVAL’16 and QBFEVAL’17) (2019)

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