Title
Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics
Abstract
We present Lytic, a domain-independent, faceted visual analytic (VA) system for interactive exploration of large datasets. It combines a flexible UI that adapts to arbitrary character-separated value (CSV) datasets with algorithmic preprocessing to compute unsupervised dimension reduction and cluster data from high-dimensional fields. It provides a variety of visualization options that require minimal user effort to configure and a consistent user experience between visualization types and underlying datasets. Filtering, comparison and visualization operations work in concert, allowing users to hop seamlessly between actions and pursue answers to expected and unexpected data hypotheses.
Year
DOI
Venue
2013
10.1145/2501511.2501518
IDEA@KDD
Keywords
Field
DocType
consistent user experience,underlying datasets,large datasets,high-dimensional algorithmic analysis,faceted visual analytics,visualization operation,visualization option,visualization type,arbitrary character-separated value,minimal user effort,unexpected data hypothesis,cluster data,visual analytics
Data mining,User experience design,Dimensionality reduction,Visualization,Computer science,Filter (signal processing),Visual analytics,Preprocessor,Interactive visual analysis,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
27
Authors
5
Name
Order
Citations
PageRank
Edward Clarkson1887.53
Jaegul Choo255646.81
John Turgeson300.34
Ray Decuir400.34
Haesun Park53546232.42