Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics最新文献

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Interactive visual analytics for high dimensional data 高维数据的交互式可视化分析

Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics Pub Date : 2013-08-11 DOI: 10.1145/2501511.2501514

Haesun Park

{"title":"Interactive visual analytics for high dimensional data","authors":"Haesun Park","doi":"10.1145/2501511.2501514","DOIUrl":"https://doi.org/10.1145/2501511.2501514","url":null,"abstract":"Many modern data sets can be represented in high dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from numerical linear algebra and optimization. Visual analytics approaches have contributed greatly to data understanding and analysis due to utilization of both automated algorithms and human's quick visual perception and interaction. However, visual analytics targeting high dimensional large-scale data has been challenging due to low dimensional screen space with limited pixels to represent data. Among various computational techniques supporting visual analytics, dimension reduction and clustering have played essential roles by reducing the dimension and volume to visually manageable scales. In this talk, we present some of the key foundational methods for supervised dimension reduction such as linear discriminant analysis (LDA), dimension reduction and clustering/topic discovery by nonnegative matrix factorization (NMF), and visual spatial alignment for effective fusion and comparisons by Orthogonal Procrustes. We demonstrate how these methods can effectively support interactive visual analytic tasks that involve large-scale document and image data sets.","PeriodicalId":126062,"journal":{"name":"Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128806335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online spatial data analysis and visualization system 在线空间数据分析和可视化系统

Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics Pub Date : 2013-08-11 DOI: 10.1145/2501511.2501522

Yun Lu, Mingjin Zhang, Tao Li, Yudong Guang, N. Rishe

引用次数: 22

A process-centric data mining and visual analytic tool for exploring complex social networks 一个以流程为中心的数据挖掘和可视化分析工具，用于探索复杂的社会网络

Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics Pub Date : 2013-08-11 DOI: 10.1145/2501511.2501519

Denis Dimitrov, L. Singh, J. Mann

{"title":"A process-centric data mining and visual analytic tool for exploring complex social networks","authors":"Denis Dimitrov, L. Singh, J. Mann","doi":"10.1145/2501511.2501519","DOIUrl":"https://doi.org/10.1145/2501511.2501519","url":null,"abstract":"Social scientists and observational scientists have a need to analyze complex network data sets. Examples of such exploratory tasks include: finding communities that exist in the data, comparing results from different graph mining algorithms, identifying regions of similarity or dissimilarity in the data sets, and highlighting nodes with important centrality properties. While many methods, algorithms, and visualizations exist, the capability to apply and combine them for ad-hoc visual exploration or as part of an analytic workflow process is still an open problem that needs to be addressed to help scientists, especially those without extensive programming knowledge. In this paper, we present Invenio-Workflow, a tool that supports exploratory analysis of network data by integrating workflow, querying, data mining, statistics, and visualization to enable scientific inquiry. Invenio-Workflow can be used to create custom exploration tasks, in addition to the standard task templates. After describing the features of the system, we illustrate its utility through several use cases based on networks from different domains.","PeriodicalId":126062,"journal":{"name":"Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129645319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

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