This survey provides a description of algorithms to reorder visual matrices of tabular data and adjacency matrix of Networks. The goal of this survey is to provide a comprehensive list of reordering ...algorithms published in different fields such as statistics, bioinformatics, or graph theory. While several of these algorithms are described in publications and others are available in software libraries and programs, there is little awareness of what is done across all fields. Our survey aims at describing these reordering algorithms in a unified manner to enable a wide audience to understand their differences and subtleties. We organize this corpus in a consistent manner, independently of the application or research field. We also provide practical guidance on how to select appropriate algorithms depending on the structure and size of the matrix to reorder, and point to implementations when available.
Temporal MDS Plots for Analysis of Multivariate Data Jäckle, Dominik; Fischer, Fabian; Schreck, Tobias ...
IEEE transactions on visualization and computer graphics,
01/2016, Letnik:
22, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Multivariate time series data can be found in many application domains. Examples include data from computer networks, healthcare, social networks, or financial markets. Often, patterns in such data ...evolve over time among multiple dimensions and are hard to detect. Dimensionality reduction methods such as PCA and MDS allow analysis and visualization of multivariate data, but per se do not provide means to explore multivariate patterns over time. We propose Temporal Multidimensional Scaling (TMDS), a novel visualization technique that computes temporal one-dimensional MDS plots for multivariate data which evolve over time. Using a sliding window approach, MDS is computed for each data window separately, and the results are plotted sequentially along the time axis, taking care of plot alignment. Our TMDS plots enable visual identification of patterns based on multidimensional similarity of the data evolving over time. We demonstrate the usefulness of our approach in the field of network security and show in two case studies how users can iteratively explore the data to identify previously unknown, temporally evolving patterns.
•Build a large-scale 3D shape retrieval benchmark that supports multi-modal queries.•Evaluate the 26 3D shape retrieval methods using 3 types of metrics.•Solicit and identify state-of-the-art methods ...and promising related techniques.•Perform detailed analysis on diverse methods w.r.t accuracy and efficiency.•Make benchmark and evaluation tools freely available to the community.
Large-scale 3D shape retrieval has become an important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale comprehensive and sketch-based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large-scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain-specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query-by-Model or Query-by-Sketch 3D shape retrieval methods and to solicit state-of-the-art approaches, we perform a more comprehensive comparison of twenty-six (eighteen originally participating algorithms and eight additional state-of-the-art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites (http://www.itl.nist.gov/iad/vug/sharp/contest/2014/Generic3D/, 2014, http://www.itl.nist.gov/iad/vug/sharp/contest/2014/SBR/, 2014).
•Build a small scale and a large scale sketch-based 3D model retrieval benchmark.•Evaluate 15 best sketch-based 3D model retrieval algorithms on the two benchmarks.•Solicit and identify the ...state-of-the-art methods and promising related techniques.•Incisive analysis on diverse methods w.r.t scalability and efficiency performance.•The benchmarks and evaluation tools provide good reference to the related community.
Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval methods have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites 1,2.
Guidance in the human–machine analytics process Collins, Christopher; Andrienko, Natalia; Schreck, Tobias ...
Visual informatics (Online),
September 2018, 2018-09-00, 2018-09-01, Letnik:
2, Številka:
3
Journal Article
Recenzirano
Odprti dostop
In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated ...model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.
The development of effective content-based multimedia search systems is an important research issue due to the growing amount of digital audio-visual information. In the case of images and video, the ...growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.
Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with ...interactive visualisations. Using appropriate techniques, analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful partitions. While this principle has been implemented either for unsupervised, semi-supervised, or supervised machine learning tasks, the combination of all three methodologies remains challenging.
In this paper, a visual analytics approach is presented, combining a variety of machine learning capabilities with four linked visualisation views, all integrated within the mVis (multivariate Visualiser) system. The available palette of techniques allows an analyst to perform exploratory data analysis on a multivariate dataset and divide it into meaningful labelled partitions, from which a classifier can be built. In the workflow, the analyst can label interesting patterns or outliers in a semi-supervised process supported by active learning. Once a dataset has been interactively labelled, the analyst can continue the workflow with supervised machine learning to assess to what degree the subsequent classifier has effectively learned the concepts expressed in the labelled training dataset. Using a novel technique called automatic dimension selection, interactions the analyst had with dimensions of the multivariate dataset are used to steer the machine learning algorithms.
A real-world football dataset is used to show the utility of mVis for a series of analysis and labelling tasks, from initial labelling through iterations of data exploration, clustering, classification, and active learning to refine the named partitions, to finally producing a high-quality labelled training dataset suitable for training a classifier. The tool empowers the analyst with interactive visualisations including scatterplots, parallel coordinates, similarity maps for records, and a new similarity map for partitions.
In this paper, we present a new approach for generic 3D shape retrieval based on a mesh partitioning scheme. Our method combines a mesh global description and mesh partition descriptions to represent ...a 3D shape. The partitioning is useful because it helps us to extract additional information in a more local sense. Thus, part descriptions can mitigate the semantic gap imposed by global description methods. We propose to find spatial agglomerations of local features to generate mesh partitions. Hence, the definition of a distance function is stated as an optimization problem to find the best match between two shape representations. We show that mesh partitions are representative and therefore it helps to improve the effectiveness in retrieval tasks. We present exhaustive experimentation using the SHREC'09 Generic Shape Retrieval Benchmark.
Display omitted
•We propose a simple and effective partitioning algorithm for 3D meshes.•The use of part descriptions enhances the use of global descriptors.•We define a distance as an optimization problem, including both linear and quadratic constraints.•Our experiments show that the partitioning algorithm has a great influence in the final effectiveness of retrieval tasks.
The understanding of health-related information is essential for making informed decisions. However, providing health information in an understandable format for everyone is challenging due to ...differences in consumers' health status, disease knowledge, skills, and preferences. Tailoring health information to individual needs can improve comprehension and increase health literacy.
The aim of our research was to analyze the extent to which consumers can customize consumer health information materials (CHIMs) for type-2 diabetes mellitus through various media types.
We conducted a comprehensive search for various CHIMs across various media types, such as websites, apps, videos, and printed or printable forms. A representative sample of CHIMs was obtained for analysis through blocked randomization across the various media types. We conducted a quantitative content analysis to determine the frequency of user-centered customization options. Cross-comparisons were made to identify trends and variations in modifiable features among the media.
In our representative sample of 114 CHIMs, we identified a total of 24 modifiable features, which we grouped into five main categories: (i) language, (ii) text, (iii) audiovisual, (iv) presentation, and (v) medical content. Videos offered the most customization opportunities (95%), while 47% of websites and 26% of apps did not allow users to tailor health information. None of the printed or printable materials provided the option to customize the information. Overall, 65% of analyzed CHIMs did not allow users to tailor health information according to their needs.
Our results show that CHIMs for type-2 diabetes mellitus could be significantly improved by providing more customization options for users. Further research is needed to investigate the effectiveness and usability of these options to enhance the development and appropriate provision of modifiable features in health information.
PDF
