Estimation

Estimation.bib

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+@inproceedings{redmondVisualCuesEstimation2019b,
+  title = {Visual {{Cues}} in {{Estimation}} of {{Part-To-Whole Comparisons}}},
+  booktitle = {2019 {{IEEE Visualization Conference}} ({{VIS}})},
+  author = {Redmond, Stephen},
+  year = {2019},
+  month = oct,
+  pages = {1--5},
+  doi = {10.1109/VISUAL.2019.8933718},
+  urldate = {2024-03-04},
+  abstract = {Pie charts were first published in 1801 by William Playfair and have caused some controversy since. Despite the suggestions of many experts against their use, several empirical studies have shown that pie charts are at least as good as alternatives. From Brinton to Few on one side and Eells to Kosara on the other, there appears to have been a hundred-year war waged on the humble pie. In this paper a set of experiments are reported that compare the performance of pie charts and horizontal bar charts with various visual cues. Amazon's Mechanical Turk service was employed to perform the tasks of estimating segments in various part-to-whole charts. The results lead to recommendations for data visualization professionals in developing dashboards.},
+  keywords = {Bars,Data visualization,Empirical studies in visualization,Estimation,Human computer interaction,Human-centered computing,Indexes,Task analysis,Visualization,Visualization design and evaluation methods},
+  file = {/Users/cbbcbail/Zotero/storage/TP8HB55J/Redmond - 2019 - Visual Cues in Estimation of Part-To-Whole Compari.pdf}
+}
+
+@article{rubio-sanchezAxisCalibrationImproving2014,
+  title = {Axis {{Calibration}} for {{Improving Data Attribute Estimation}} in {{Star Coordinates Plots}}},
+  author = {{Rubio-S{\'a}nchez}, Manuel and Sanchez, Alberto},
+  year = {2014},
+  month = dec,
+  journal = {IEEE Transactions on Visualization and Computer Graphics},
+  volume = {20},
+  number = {12},
+  pages = {2013--2022},
+  issn = {1941-0506},
+  doi = {10.1109/TVCG.2014.2346258},
+  urldate = {2024-03-04},
+  abstract = {Star coordinates is a well-known multivariate visualization method that produces linear dimensionality reduction mappings through a set of radial axes defined by vectors in an observable space. One of its main drawbacks concerns the difficulty to recover attributes of data samples accurately, which typically lie in the [0], [1] interval, given the locations of the low-dimensional embeddings and the vectors. In this paper we show that centering the data can considerably increase attribute estimation accuracy, where data values can be read off approximately by projecting embedded points onto calibrated (i.e., labeled) axes, similarly to classical statistical biplots. In addition, this idea can be coupled with a recently developed orthonormalization process on the axis vectors that prevents unnecessary distortions. We demonstrate that the combination of both approaches not only enhances the estimates, but also provides more faithful representations of the data.},
+  keywords = {Attribute value estimation,Axis calibration,Biplots,Calibration,Data centering,Data visualization,Estimation error,Linear systems,Multivariate regression,Orthographic projection,RadViz,Star Coordinates},
+  file = {/Users/cbbcbail/Zotero/storage/PKFGT7HX/Rubio-Sánchez and Sanchez - 2014 - Axis Calibration for Improving Data Attribute Esti.pdf;/Users/cbbcbail/Zotero/storage/BH48CMQW/6875998.html}
+}
+
+@inproceedings{suzukiScatterplotBasedVisualizationTool2016,
+  title = {A {{Scatterplot-Based Visualization Tool}} for {{Regression Analysis}}},
+  booktitle = {2016 20th {{International Conference Information Visualisation}} ({{IV}})},
+  author = {Suzuki, Chie and Itoh, Takayuki and Umezu, Keisuke and Motohashi, Yousuke},
+  year = {2016},
+  month = jul,
+  pages = {75--80},
+  issn = {2375-0138},
+  doi = {10.1109/IV.2016.63},
+  urldate = {2024-03-04},
+  abstract = {Regression analysis has been widely applied to various academic and industrial fields. Applications of regression analysis include medical problems such as health estimation, environmental problems such as disaster prediction and energy consumption estimation, and business/economic analytics. Here, accuracy and quality of regression analysis strongly depend on relevancy between input explanatory variables and actual objective functions. It often happens that several explanatory variables are well correlated with objective functions while others do not well correlated, and therefore accuracy of regression analysis may improve by removing unnecessary explanatory variables. This paper presents a scatterplot-based regression analysis tool. This tool visualizes the distribution of errors between actual and estimated values of objective functions, and provides user interfaces to explore the relationships between explanatory variables and the errors. This paper introduces examples of the visualization results using the presented tool with actual and estimated revenues at a store.},
+  keywords = {Data visualization,Estimation,Linear programming,Mathematical model,Regression analysis,scatterplots,Three-dimensional displays,Visualization},
+  file = {/Users/cbbcbail/Zotero/storage/5DVR7Y2N/Suzuki et al. - 2016 - A Scatterplot-Based Visualization Tool for Regress.pdf;/Users/cbbcbail/Zotero/storage/3SQCVHVH/7557907.html}
+}
+
+@article{ventocillaComparativeUserStudy2020,
+  title = {A Comparative User Study of Visualization Techniques for Cluster Analysis of Multidimensional Data Sets},
+  author = {Ventocilla, Elio and Riveiro, Maria},
+  year = {2020},
+  month = oct,
+  journal = {Information Visualization},
+  volume = {19},
+  number = {4},
+  pages = {318--338},
+  publisher = {{SAGE Publications}},
+  issn = {1473-8716},
+  doi = {10.1177/1473871620922166},
+  urldate = {2024-03-04},
+  abstract = {This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters,  k , embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of  k  as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with  k  values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward's hierarchical clustering) are likely to lead to estimates of  k  that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.},
+  langid = {english},
+  file = {/Users/cbbcbail/Zotero/storage/LFULNBAK/Ventocilla and Riveiro - 2020 - A comparative user study of visualization techniqu.pdf}
+}

README.md

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 # [Annotated Bibliography of Tabular Visualization](https://pages.graphics.cs.wisc.edu/Tables/)
 
-This repository contains an annotated bibliography of literature related to tabular visualization. The literature is organized into categories and sorted by date of publication within categories. A short summary, and notes about how the work relates to - and is interesting or important to - our particular interests is followed by a citation in IEEE format. The author and year are emphasized for easy lookup and the DOI links are provided.
+This repository contains an annotated bibliography of literature related to estimation in visualization. The literature is organized into categories and sorted by date of publication within categories. A short summary, and notes about how the work relates to - and is interesting or important to - our particular interests is followed by a citation in IEEE format. The author and year are emphasized for easy lookup and the DOI links are provided.
 
 ## Generating the Annotated Bibliography