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ZoomOut: Analyzing Multiple Networks as Single Nodes

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第5期12卷 1213-1216页

作者： Athanasiadis, Emmanouil I. Bourdakou, Marilena M. Spyrou, George M. Acad Athens Ctr Syst Biol Biomed Res Fdn Athens 11527 4 Greece Univ Athens Dept Informat & Telecommun Ilissia 15784 Greece

We have developed ZoomOut web server in order to provide the research community with a tool for analysis, visualization and clustering of networks as a super network, based on their calculated feature properties. Networks can be analysed and be further treated as single nodes in a super network that describe their relations. Specifically, the user interface is divided into three main sections: the Workspace, the Networks Feature Calculations and the Clustering Networks section. In the Workspace section, users are able to upload and manage multiple networks for further processing. In the Networks Feature Calculations section, a variety of network properties are calculated as features for each uploaded network. In the Clustering Networks section, users are able to apply clustering by selecting from the list of previously calculated features. All processed networks can also be visualized as a super interactive network, were interconnections among networks are based on the calculated clustering distances. To the best of our knowledge, this is the first available web-service that allows users to manage, quantify and visualize multiple networks at the same time, handling them as parts of a larger network with properties calculated in an upper scale. The ZoomOut web-application is available at http://***/Bioserver/ZoomOut.

关键词： Clustering classification and association rules data and knowledge visualization Feature extraction or construction Graphs and networks

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Task-oriented analysis and visualization of correlation patterns in multi-sensor time series

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knowledge-BASED SYSTEMS 2024年 289卷

作者： Sun, Jiancheng Li, Xiaohe Jin, Yongnu Dai, Liyun Peng, Xiangdong Chen, Chunlin Jiangxi Univ Finance & Econ Sch Software & Internet Things Engn Nanchang 330013 Peoples R China Xi An Shiyou Univ Coll Comp Sci Xian 710065 Peoples R China

Time series data are usually collected through instruments equipped with sensors. For multi-sensor time series (MSTS), it is crucial to identify the factors that affect tasks such as classification and regression. To better understand the mechanisms affecting the task, we extracted the correlation patterns between sensors and represented them as symmetric positive definite matrices. The correlation patterns were then transformed into vector form to serve as input features for regression or classification models, and the models were trained for each sensor. Finally, we leveraged the interpretability of the models to analyze and visualize the correlation patterns at both micro and macro scales. By integrating the explanatory power of the models with correlation patterns, we could interpret the task in terms of time and space, providing valuable insights for exploring the underlying data rules. We evaluated our proposed method using both synthetic and real data, and the simulation results confirmed its effectiveness.

关键词： Multi -sensor Time series analysis data and knowledge visualization Pattern analysis

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knowledge discovery and visualisation framework using machine learning for music information retrieval from broadcast radio data

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115236-115236页

作者： Furner, Michael Islam, Md Zahidul Li, Chang-Tsun Charles Sturt Univ Sch Comp & Math Panorama Ave Bathurst NSW 2795 Australia Deakin Univ Fac Sci Engn & Built Environm Geelong Vic Australia

Music radio data is currently underutilised in radio program management. Software tools that listen to and analyse music airplay are in many markets nonexistent, limited, or unaffordable. In this paper we present a novel knowledge discovery and visualisation framework for broadcast radio, ZeitMetric. The ZeitMetric framework uses machine learning and music information retrieval techniques to label radio audio automatically for knowledge discovery. The framework incorporates a novel music dataset collection technique (MusiGrab) to leverage online music services for ground-truth data, as well as a novel knowledge visualisation and presentation technique based on self-organizing maps (ZeitViz). The framework is compared to what little literature relating to this topic exists, and a set of requirements for a high-quality broadcast radio knowledge discovery is developed. MusiGrab specifically is compared to an existing static music information retrieval dataset and shown to offer superior results in this context. Future research directions using and extending the framework are also discussed. On acceptance of the paper, code for a use-case of the MusiGrab dataset collection technique will be released on GitHub.

关键词： data mining Machine learning Sound and music computing Signal processing systems Software Architectures data and knowledge visualization Record classification

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Interactive visual study for residential energy consumption data

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JOURNAL OF CLEANER PRODUCTION 2022年 366卷

作者： Al-Kababji, Ayman Alsalemi, Abdullah Himeur, Yassine Fernandez, Rachael Bensaali, Faycal Amira, Abbes Fetais, Noora Qatar Univ Dept Elect Engn Doha Qatar Qatar Univ Dept Comp Sci & Engn Doha Qatar Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates De Montfort Univ Inst Artificial Intelligence Leicester Leics England

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions' responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic.

关键词： data and knowledge visualization Energy-aware systems Energy efficiency Mobile applications Evaluation studies visualizations comparison

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Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way Clustering of Microarray data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2009年第2期6卷 244-259页

作者： Shaik, Jahangheer Yeasin, Mohammed Washington Univ Dept Pathol & Immunol St Louis MO 63130 USA Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA Univ Memphis Bioinformat Dept Memphis TN 38152 USA

This paper presents Fuzzy-Adaptive-Subspace-Iteration-based Two-way Clustering (FASIC) of microarray data for finding differentially expressed genes (DEGs) from two-sample microarray experiments. The concept of fuzzy membership is introduced to transform the hard adaptive subspace iteration (ASI) algorithm into a fuzzy-ASI algorithm to perform two-way clustering. The proposed approach follows a progressive framework to assign a relevance value to genes associated with each cluster. Subsequently, each gene cluster is scored and ranked based on its potential to provide a correct classification of the sample classes. These ranks are converted into P values using the R-test, and the significance of each gene is determined. A fivefold validation is performed on the DEGs selected using the proposed approach. Empirical analyses on a number of simulated microarray data sets are conducted to quantify the results obtained using the proposed approach. To exemplify the efficacy of the proposed approach, further analyses on different real microarray data sets are also performed.

关键词： Clustering classification and association rules data mining data and knowledge visualization feature extraction or construction

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Effect of color properties in multiple time series graph comprehension

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APPLIED ERGONOMICS 2022年 103卷 103808页

作者： Seva, Rosemary R. Wu, Judy Ann G. Chinjen, Katrina K. Estoista, Nicole Anne Therese P. Salle Univ Ind & Syst Engn Dept Manila Philippines

Multiple time series graphs are used prevalently in representing business and research data, but the use of color properties to visualize them to enhance comprehension is limited. This study explored the effect of hue and lightness in representing 4-time series data in relation to response time (RT) and accuracy. Two types of palettes were developed for each experiment: monochrome and multi-hue. The three sets of monochrome palettes created were red, green, and blue, while four equidistant hues in the color wheel were used in the multi-hue palette: red, blue, green, and purple. A total of forty people participated in the two experiments. Participants performed two tasks for both experiments: maximum and discrimination tasks. The monochrome experiment showed the primacy of green in terms of RT and accuracy in the discrimination task. RT and accuracy were significantly affected by lightness in the multi-hue experiment. For both tasks, RT was longer for 20% lightness and lowest at 60% lightness. Accuracy results were also consistent with RT. In the discrimination task, participants made more errors in 20% lightness and the highest accuracy for 60% and 80%.

关键词： Color data and knowledge visualization Graphics recognition and interpretation Time series analysis

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Visual Analytics: Why Now?

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Information visualization 2007年第1期6卷 104 - 106页

作者： James (Jim) J. Thomas Pacific Northwest National Laboratory Richland WA U.S.A.

An emerging field of study, visual analytics, is briefly described, including its motivations and the emerging partnerships that are bringing the best talents and technologies to missions such as homeland security and... 详细信息

关键词： Visual analytics data and knowledge visualization visualization techniques and methodologies

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RuleViz: a model for visualizing knowledge discovery process 00

RuleViz: a model for visualizing knowledge discovery process

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Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining

作者： Jianchao Han Nick Cercone Department of Computer Science University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada

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knowledge visualization in hepatitis study 06

Knowledge visualization in hepatitis study

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Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60

作者： DucDung Nguyen TuBao Ho Saori Kawasaki School of Knowledge Science Japan Advanced Institute of Science and Technology Nomi city Ishikawa Japan

ISBN: (纸本)9781920682415

Evidence-based medicine (EBM) is a shift in which medicine from being based on individual experience of doctors to evidence with clear background. In this shift, data mining can play a significant role as its ability of uncovering medical evidence from large volumes of medical data. Recognizing the crucial role of visualization in discovering such evidences, this work presents some developed tools integrated in our data mining system D2MS for appropriately visualizing knowledge, and their usage in hepatitis study. We emphasize on our two rule visualizers, one for individual rule and the other for rule in its relations with the others.

关键词： data and knowledge visualization hepatitis study model selection

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visualization support for a user-centered KDD process 02

Visualization support for a user-centered KDD process

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Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining

作者： TuBao Ho TrongDung Nguyen DungDuc Nguyen Japan Advanced Institute of Science and Technology Tatsunokuchi Ishikawa 923-1292 Japan

ISBN: (纸本)9781581135671

Viewing knowledge discovery as a user-centered process that requires an effective collaboration between the user and the discovery system, our work aims to support an active role of the user in that process by developing synergistic visualization tools integrated in our discovery system D2MS. These tools provide an ability of visualizing the entire process of knowledge discovery in order to help the user with data preprocessing, selecting mining algorithms and parameters, evaluating and comparing discovered models, and taking control of the whole discover process. Our case-studies with two medical datasets on meningitis and stomach cancer show that, with visualization tools in D2MS, the user gains better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge.

关键词： knowledge discovery process model selection the user's active role data and knowledge visualization

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