Chaoran Fan

PhD student

 Team Hauser

PhD student since October 2015, my research topic is about semi-automatic visual data analysis. I am currently visiting with Professor Helwig Hauser in Delft University of Technology, Netherlands.



    [PDF] [DOI] [Bibtex]
    author={Fan, Chaoran and Hauser, Helwig},
    journal={IEEE Computer Graphics and Applications},
    title={On sketch-based selections from scatterplots using KDE, compared to Mahalanobis and CNN brushing},
    abstract = {"Fast and accurate brushing is crucial in visual data exploration and sketch-based solutions are successful methods. In this paper, we detail a solution, based on kernel density estimation (KDE), which computes a data subset selection in a scatterplot from a simple click-and-drag interaction. We explain, how this technique relates to two alternative approaches, i.e., Mahalanobis brushing and CNN brushing. To study this relation, we conducted two user studies and present both a quantitative three-fold comparison as well as additional details about the prevalence of all possible cases in that each technique succeeds/fails. With this, we also provide a comparison between empirical modeling and implicit modeling by deep learning in terms of accuracy, efficiency, generality and interpretability."},
    pdf = "pdfs/Fan-2021-brushingComparison.pdf",
    images = "images/Fan-2021-brushingComparison.png",
    thumbnails = "images/Fan-2021-brushingComparison.png",


    [PDF] [DOI] [Bibtex]
    author={Fan, Chaoran and Matkovic, Kresimir and Hauser, Helwig},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    title={Sketch-based fast and accurate querying of time series using parameter-sharing LSTM networks},
    abstract = {"Sketching is one common approach to query time series data for patterns of interest. Most existing solutions for matching the data with the interaction are based on an empirically modeled similarity function between the user's sketch and the time series data with limited efficiency and accuracy. In this paper, we introduce a machine learning based solution for fast and accurate querying of time series data based on a swift sketching interaction. We build on existing LSTM technology (long short-term memory) to encode both the sketch and the time series data in a network with shared parameters. We use data from a user study to let the network learn a proper similarity function. We focus our approach on perceived similarities and achieve that the learned model also includes a user-side aspect. To the best of our knowledge, this is the first data-driven solution for querying time series data in visual analytics. Besides evaluating the accuracy and efficiency directly in a quantitative way, we also compare our solution to the recently published Qetch algorithm as well as the commonly used dynamic time warping (DTW) algorithm.."},
    pdf = "pdfs/Fan-2020-sketchingQuery.pdf",
    images = "images/Fan-2020-sketchingQuery.png",
    thumbnails = "images/Fan-2020-sketchingQuery.png",


    [PDF] [Bibtex]
    title={Personalized Sketch-Based Brushing in Scatterplots},
    author={Chaoran Fan and Helwig Hauser},
    journal={IEEE Computer Graphics and Applications},
    thumbnails = "images/personalizedBrush.png",
    abstract="Brushing is at the heart of most modern visual analytics solutions and effective and efficient brushing is crucial for successful interactive data exploration and analysis. As the user plays a central role in brushing, several data-driven brushing tools have been designed that are based on predicting the user’s brushing goal. All of these general brushing models learn the users’ average brushing preference, which is not optimal for every single user. In this paper, we propose an innovative framework that offers the user opportunities to improve the brushing technique while using it. We realized this framework with a CNN-based brushing technique and the result shows that with additional data from a particular user, the model can be refined (better performance in terms of accuracy), eventually converging to a personalized model based on a moderate amount of retraining."
    [PDF] [DOI] [Bibtex]
    author = "Chaoran Fan and Helwig Hauser",
    title = "On KDE-based brushing in scatterplots and how it compares to CNN-based brushing",
    booktitle = "Proceedings of MLVis: Machine Learning Methods in Visualisation for Big Data",
    year = "2019",
    publisher = "Eurographics Association",
    abstract = "In this paper, we investigate to which degree the human should be involved into the model design and how good the empirical model can be with more careful design. To find out, we extended our previously published Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information that is captured by the kernel density estimation (KDE). Based on this work, we then include a short discussion between the empirical model, designed in detail by an expert and the deep learning-based model that is learned from user data directly",
    pdf = "pdfs/On-KDE-based-brushing-in-scatterplotsand-how-it-compares-to-CNN-based-brushing.pdf",
    images = "images/pic-2.png",
    thumbnails = "images/pic-2.png",
    doi = "10.2312/mlvis.20191157",


    [PDF] [DOI] [Bibtex]
    @ARTICLE {cnn-brush,
    author = "Fan, Chaoran and Hauser, Helwig",
    title = "{Fast and Accurate CNN-based Brushing in Scatterplots}",
    journal = "Computer Graphics Forum (Eurovis 2018)",
    year = "2018",
    abstract = "Brushing plays a central role in most modern visual analytics solutions and effective and efficient techniques for data selection are key to establishing a successful human-computer dialogue. With this paper, we address the need for brushing techniques that are both fast, enabling a fluid interaction in visual data exploration and analysis, and also accurate, i.e., enabling the user to effectively select specific data subsets, even when their geometric delimination is non-trivial. We present a new solution for a near-perfect sketch-based brushing technique, where we exploit a convolutional neural network (CNN) for estimating the intended data selection from a fast and simple click-and-drag interaction and from the data distribution in the visualization. Our key contributions include a drastically reduced error rate-now below 3%, i.e., less than half of the so far best accuracy- and an extension to a larger variety of selected data subsets, going beyond previous limitations due to linear estimation models.",
    pdf = "pdfs/eurovis18.pdf",
    images = "images/cnn.png",
    thumbnails = "images/cnn.png",
    publisher = "The Eurographics Association and John Wiley and Sons Ltd.",
    issn = "1467-8659",
    doi = "10.1111/cgf.13405"


    [PDF] [DOI] [Bibtex]
    @INPROCEEDINGS {newMahalanobisBrush,
    author = "Fan, Chaoran and Hauser, Helwig",
    title = "{User-study Based Optimization of Fast and Accurate Mahalanobis Brushing in Scatterplots}",
    booktitle = "Vision, Modeling & Visualization",
    year = "2017",
    editor = "Matthias Hullin and Reinhard Klein and Thomas Schultz and Angela Yao",
    publisher = "The Eurographics Association",
    abstract = "Brushing is at the heart of most modern visual analytics solutions with coordinated, multiple views and effective brushing is crucial for swift and efficient processes in data exploration and analysis. Given a certain data subset that the user wishes to brush in a data visualization, traditional brushes are usually either accurate (like the lasso) or fast (e.g., a simple geometry like a rectangle or circle). In this paper, we now present a new, fast and accurate brushing technique for scatterplots, based on the Mahalanobis brush, which we have extended and then optimized using data from a user study. We explain the principal, sketchbased model of our new brushing technique (based on a simple click-and-drag interaction), the details of the user study and the related parameter optimization, as well as a quantitative evaluation, considering efficiency, accuracy, and also a comparison with the original Mahalanobis brush.",
    pdf = "pdfs/vmv-final.pdf",
    images = "images/Mahalanobis.png",
    thumbnails = "images/Mahalanobis.png",
    isbn = "978-3-03868-049-9",
    doi = "10.2312/vmv.20171262"