Towards a User-Defined Visual-Interactive Definition of Similarity Functions for Mixed Data

被引：0

作者：

Bernard, Juergen ^{[1
]}

Sessler, David ^{[1
]}

Behrisch, Michael ^{[2
]}

Hutter, Marco ^{[1
]}

Schreck, Tobias ^{[2
]}

Kohlhammer, Joern ^{[1
,3
]}

机构：

[1] Fraunhofer IGD, Darmstadt, Germany

[2] Univ Konstanz, Constance, Germany

[3] Tech Univ Darmstadt, Darmstadt, Germany

来源：

2014 IEEE CONFERENCE ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY (VAST) | 2014年

关键词：

I.5.3 [Computing Methodologies]: Pattern Recognition; Clustering; H.5.2 [Information Systems]: Information Interfaces and Presentation; User Interfaces;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The creation of similarity functions based on visual-interactive user feedback is a promising means to capture the mental similarity notion in the heads of domain experts. In particular, concepts exist where users arrange multivariate data objects on a 2D data landscape in order to learn new similarity functions. While systems that incorporate numerical data attributes have been presented in the past, the remaining overall goal may be to develop systems also for mixed data sets. In this work, we present a feedback model for categorical data which can be used alongside of numerical feedback models in future.

引用

页码：227 / 228

页数：2

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