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» Apolo: making sense of large network data by combining rich ...
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CHI
2011
ACM
12 years 8 months ago
Apolo: making sense of large network data by combining rich user interaction and machine learning
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduc...
Duen Horng Chau, Aniket Kittur, Jason I. Hong, Chr...
KDD
2012
ACM
226views Data Mining» more  KDD 2012»
11 years 7 months ago
TourViz: interactive visualization of connection pathways in large graphs
We present TOURVIZ, a system that helps its users to interactively visualize and make sense in large network datasets. In particular, it takes as input a set of nodes the user spe...
Duen Horng Chau, Leman Akoglu, Jilles Vreeken, Han...
VIS
2009
IEEE
399views Visualization» more  VIS 2009»
14 years 6 months ago
Visual Human+Machine Learning
In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combine...
Raphael Fuchs, Jürgen Waser, Meister Eduard Gr...
KDD
2006
ACM
272views Data Mining» more  KDD 2006»
14 years 5 months ago
YALE: rapid prototyping for complex data mining tasks
KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring th...
Ingo Mierswa, Michael Wurst, Ralf Klinkenberg, Mar...
LWA
2007
13 years 6 months ago
Know the Right People? Recommender Systems for Web 2.0
Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. However, the rapid increase in siz...
Stefan Siersdorfer, Sergej Sizov, Paul Clough