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A Study on Performance Assessment of Essential Clustering Algorithms for the Interactive Visual Analysis Toolkit Invexстатья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 10 июня 2020 г.
- Авторы: Mikhail Titov, Maria Grigorieva, Aleksandr Alekseev, Nikita Belov, Timofei Galkin, Dmitry Grin, Tatiana Korchuganova, Sergey Zhumatiy
- Сборник: CEUR WORKSHOP PROCEEDINGS
- Серия: Proceedings of the 27th Symposium on Nuclear Electronics and Computing
- Том: 2507
- Год издания: 2019
- Первая страница: 434
- Последняя страница: 438
- Аннотация: Interactive visual analysis tools bring the ability of the real-time discovery of knowledge in large andcomplex datasets using visual analytics. It involves multiple iterations of data processing using variousdata handling approaches and the efficiency of the whole chain of the analysis process depends on theperformance of chosen techniques and related implementations, as well as the quality of appliedmethods. Stages, where data processing includes intellectual handling (i.e., data mining and machinelearning), which are the most resource-intensive, require a distinct attention for evaluation of differentapproaches. Clustering is one such machine learning technique that is commonly used to discovergroups of data objects for further analysis. This work is focused on evaluation of clustering algorithmswithin the interactive visual analysis toolkit InVEx (Interactive Visual Explorer). InVEx represents avisual analytics approach aimed at cluster analysis and in-depth study of implicit correlations betweenmultidimensional data objects. It is originally designed to enhance the analysis of computing metadataof the ATLAS experiment at the LHC for operational needs, but it also provides the same capabilitiesfor other domains to analyze large amounts of multidimensional data. The experiments and evaluationprocesses are carried out using operational data from the supercomputer at the Lomonosov MoscowState University. These processes include benchmark tests to assess the relative performance betweenchosen clustering algorithms and corresponding metrics to assess the quality of produced clusters.Obtained results will be used as guidelines in assisting users in a process of visual analysis usingInVEx.
- Добавил в систему: Григорьева Мария Александровна