lionfish_references.bib

@article{leisch02,
    author = {Leisch, F.},
    journal = {R News},
    number = {3},
    pages = {28--31},
    pdf = {http://CRAN.R-project.org/doc/Rnews/Rnews\_2002-3.pdf},
    title = {Sweave, Part {I}: Mixing {R} And {\\LaTeX}},
    url = {http://CRAN.R-project.org/doc/Rnews/},
    volume = {2},
    year = {2002}
}

@Article{flexclust,
  author = {Friedrich Leisch},
  title = {A Toolbox for k-Centroids Cluster Analysis},
  journal = {Computational Statistics and Data Analysis},
  year = {2006},
  volume = {51},
  number = {2},
  pages = {526--544},
  doi = {10.1016/j.csda.2005.10.006},
}

@book{leisch2018market,
  title={Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful},
  author={Dolnicar, Sara and Gr{\"u}n, Bettina and Leisch, Friedrich},
  year={2018},
  publisher={Springer},
  address={Singapore}
}

@article{dolnicar2003winter,
  title={Winter Tourist Segments In Austria: Identifying Stable Vacation Styles Using Bagged Clustering Techniques},
  author={Dolnicar, Sara and Leisch, Friedrich},
  journal={Journal of Travel Research},
  volume={41},
  number={3},
  pages={281--292},
  year={2003},
  publisher={Sage Publications},
  doi = {10.1177/0047287502239037}
}

@phdthesis{cliff2009formative,
  title={A formative Index Of Segment Attractiveness: Optimising Segment Selection for Tourism Destinations},
  author={Cliff, Katie},
  year={2009},
  school={University of Wollongong},
  url={https://hdl.handle.net/10779/uow.27663669.v1}
}

@article{lundh1999introduction,
 title={An Introduction To Tkinter},
 author={Lundh, Fredrik},
 journal={URL: www. pythonware. com/library/tkinter/introduction/index. htm},
 year={1999}
}

@misc{schimansky24,
  author = {Schimansky, T.},
  title = {CustomTkinter},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/TomSchimansky/CustomTkinter}}
}


@misc{galahr,
  author = {Laa, U., Cook D.},
  title = {Galahr},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/uschiLaa/galahr}}
}


@Article{Hunter:2007,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D Graphics Environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007
}

@Article{tourr,
  title = {{tourr}: An {R} Package for Exploring Multivariate Data
    With Projections},
  author = {Hadley Wickham and Dianne Cook and Heike Hofmann and
    Andreas Buja},
  journal = {Journal of Statistical Software},
  year = {2011},
  volume = {40},
  number = {2},
  pages = {1--18},
  url = {https://doi.org/10.18637/jss.v040.i02},
}

@Manual{reticulate,
  title = {reticulate: Interface To 'Python'},
  author = {Kevin Ushey and JJ Allaire and Yuan Tang},
  year = {2024},
  note = {R package version 1.38.0, 
  https://github.com/rstudio/reticulate},
  url = {https://rstudio.github.io/reticulate/},
}


@Book{knitr,
    title = {Dynamic Documents With {R} And knitr},
    author = {Yihui Xie},
    publisher = {Chapman and Hall/CRC},
    address = {Boca Raton, Florida},
    year = {2015},
    edition = {2nd},
    doi = {10.1201/9781315382487},
  }


@Manual{shiny,
  title = {shiny: Web Application Framework for R},
  author = {Winston Chang and Joe Cheng and JJ Allaire and Carson Sievert and Barret Schloerke and Yihui Xie and Jeff Allen and Jonathan McPherson and Alan Dipert and Barbara Borges},
  year = {2024},
  note = {R package version 1.9.1.9000,
    https://github.com/rstudio/shiny},
  url = {https://shiny.posit.co/},
}

@online{plotly,
  author = {Plotly Technologies Inc.},
  title = {Collaborative Data Science},
  publisher = {Plotly Technologies Inc.},
  address = {Montreal, QC},
  year = {2015},
  url = {https://plot.ly}
}

@Manual{detourr,
  title = {detourr: Portable And Performant Tour Animations},
  author = {Casper Hart and Earo Wang},
  year = {2022},
  note = {R package version 0.1.0},
  url = {https://casperhart.github.io/detourr/},
}

@article{RJ-2023-052,
  author = {Hart, Casper and Wang, Earo},
  title = {Taking The Scenic Route: Interactive And Performant Tour Animations},
  journal = {The R Journal},
  year = {2023},
  note = {https://doi.org/10.32614/RJ-2023-052},
  doi = {10.32614/RJ-2023-052},
  volume = {15},
  issue = {2},
  issn = {2073-4859},
  pages = {307-329}
}

@article{laa2023new,
  title={New And Simplified Manual Controls for Projection And Slice Tours, With Application To Exploring Classification Boundaries In High Dimensions},
  author={Laa, Ursula and Aumann, Alex and Cook, Dianne and Valencia, German},
  journal={Journal of Computational and Graphical Statistics},
  volume={32},
  number={3},
  pages={1229--1236},
  year={2023},
  publisher={Taylor \& Francis},
  doi = {10.1080/10618600.2023.2206459}
}

@ARTICLE{Asimov1985-xr,
  title     = "{T}he {G}rand {T}our: {A} {T}ool for {V}iewing
               {M}ultidimensional {D}ata",
  author    = "Asimov, Daniel",
  abstract  = "The grand tour is a method for viewing multivariate statistical
               data via orthogonal projections onto a sequence of
               two-dimensional subspaces. The sequence of subspaces is chosen
               so that it is dense in the set of all two-dimensional subspaces.
               Desirable properties of such sequences of subspaces are
               considered, and several specific types of sequences are tested
               for rapidity of becoming dense. Tabulations are provided of the
               minimum length of a grand tour sequence necessary to achieve
               various degrees of denseness in dimensions up to 20.",
  journal   = "SIAM Journal of Scientific and Statistical Computing",
  publisher = "Society for Industrial and Applied Mathematics",
  volume    =  6,
  number    =  1,
  pages     = "128--143",
  month     =  jan,
  year      =  1985,
  url       = "http://dx.doi.org/10.1137/0906011",
  keywords  = "wiley-review-paper;high-dimensions",
  issn      = "0196-5204",
  doi       = "10.1137/0906011"
}

@article{lee2022,
author = {Lee, Stuart and Cook, Dianne and da Silva, Natalia and Laa, Ursula and Spyrison, Nicholas and Wang, Earo and Zhang, H. Sherry},
title = {The State-Of-The-Art On Tours for Dynamic Visualization Of High-Dimensional Data},
journal = {WIREs Computational Statistics},
volume = {14},
number = {4},
pages = {e1573},
keywords = {data science, data visualization, exploratory data analysis, high-dimensional data, tours},
doi = {https://doi.org/10.1002/wics.1573},
url = {https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/wics.1573},
eprint = {https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1573},
abstract = {Abstract This article discusses a high-dimensional visualization technique called the tour, which can be used to view data in more than three dimensions. We review the theory and history behind the technique, as well as modern software developments and applications of the tour that are being found across the sciences and machine learning. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis},
year = {2022}
}

@ARTICLE{Laa2020,
author = {Ursula Laa and Dianne Cook and German Valencia},
title = {A Slice Tour for Finding Hollowness In High-Dimensional Data},
journal = {Journal of Computational and Graphical Statistics},
volume = {29},
number = {3},
pages = {681-687},
year  = {2020},
publisher = {Taylor & Francis},
doi = {10.1080/10618600.2020.1777140},
  
}

@article{cook_manual_1997,
	title = {Manual Controls for High-Dimensional Data Projections},
	volume = {6},
	issn = {1061-8600},
	url = {http://www.jstor.org/stable/1390747},
	doi = {10.2307/1390747},
	abstract = {Projections of high-dimensional data onto low-dimensional subspaces provide insightful views for understanding multivariate relationships. This article discusses how to manually control the variable contributions to the projection. The user has control of the way a particular variable contributes to the viewed projection and can interactively adjust the variable's contribution. These manual controls complement the automatic views provided by a grand tour, or a guided tour, and give greatly improved flexibility to data analysts.},
	number = {4},
	urldate = {2018-04-15},
	journal = {Journal of Computational and Graphical Statistics},
	author = {Cook, Dianne and Buja, Andreas},
	year = {1997},
	pages = {464--480},
	file = {JSTOR Full Text PDF:C\:\\Users\\spyri\\Zotero\\storage\\9QIJC6VL\\Cook and Buja - 1997 - Manual Controls for High-Dimensional Data Projecti.pdf:application/pdf},
}


@article{murtagh2014ward,
  title={Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?},
  author={Murtagh, Fionn and Legendre, Pierre},
  journal={Journal of classification},
  volume={31},
  pages={274--295},
  year={2014},
  publisher={Springer},
  doi = {10.1007/s00357-014-9161-z}
}

@article{CBCH94,
        Author = {Cook, D. and Buja, A. and Cabrera, J. and Hurley, C.},
        Journal = {Journal of Computational and Graphical Statistics},
        Number = 3,
        Pages = {155--172},
        Title = {Grand {T}our And {P}rojection {Pursuit}},
        Volume = 4,
        Year = 1995,
        doi = {10.2307/1390844}
}

@article{lckl2005,
author = {Eun-Kyung Lee and Dianne Cook and Sigbert Klinke and Thomas Lumley},
title = {Projection Pursuit for Exploratory Supervised Classification},
journal = {Journal of Computational and Graphical Statistics},
volume = {14},
number = {4},
pages = {831-846},
year  = {2005},
publisher = {Taylor & Francis},
doi = {10.1198/106186005X77702}
}


@Manual{R,
     title = {R: A Language and Environment for Statistical Computing},
     author = {{R Core Team}},
     organization = {R Foundation for Statistical Computing},
     address = {Vienna, Austria},
     year = {2024},
     url = {https://www.R-project.org/}
}

@book{python,
 author = {Van Rossum, Guido and Drake, Fred L.},
 title = {Python 3 Reference Manual},
 year = {2009},
 isbn = {1441412697},
 publisher = {CreateSpace},
 address = {Scotts Valley, CA}
}

@book{ggobi,
author = {Cook, Dianne and Swayne, Deborah},
year = {2007},
month = {01},
pages = {},
title = {Interactive and Dynamic Graphics for Data Analysis: With R and GGobi},
volume = {364},
isbn = {978-0-387-71761-6},
doi = {10.1007/978-0-387-71762-3},
publisher = {Springer},
address={New York}
}

@book{keim2010mastering,
  title={Mastering the information age solving problems with visual analytics},
  author={Keim, Daniel and Kohlhammer, J{\"o}rn and Ellis, Geoffrey and Mansmann, Florian},
  year={2010},
  publisher={Eurographics Association},
  url={https://doi.org/10.2312/14803},
  doi={10.2312/14803}
}
  
@Manual{ggbeeswarm,
  title = {{ggbeeswarm}: Categorical Scatter (Violin Point) Plots},
  author = {Erik Clarke and Scott Sherrill-Mix and Charlotte Dawson},
  year = {2023},
  note = {R package version 0.7.2},
  url = {https://CRAN.R-project.org/package=ggbeeswarm},
}

@book{dolnicar2017peer,
  title={Peer-to-peer accommodation networks: Pushing the boundaries},
  author={Dolnicar, Sara},
  year={2017},
  publisher={Goodfellow Publishers}
}