Title :
Generating hypotheses of trends in high-dimensional data skeletons
Author :
Reddy, C.K. ; Pokharkar, Snehal ; Ho, Tin Kam
Abstract :
We seek an information-revealing representation for high-dimensional data distributions that may contain local trends in certain subspaces. Examples are data that have continuous support in simple shapes with identifiable branches. Such data can be represented by a graph that consists of segments of locally fit principal curves or surfaces summarizing each identifiable branch. We describe a new algorithm to find the optimal paths through such a principal graph. The paths are optimal in the sense that they represent the longest smooth trends through the data set, and jointly they cover the data set entirely with minimum overlap. The algorithm is suitable for hypothesizing trends in high-dimensional data, and can assist exploratory data analysis and visualization.
Keywords :
curve fitting; data analysis; graph theory; surface fitting; data analysis; data visualization; high-dimensional data skeleton; information-revealing representation; locally fit principal curve; optimal path; principal graph; Computer science; Data analysis; Data visualization; Mathematics; Navigation; Pixel; Shape; Skeleton; Surface fitting; Tin; G.4.1 [Mathematics of Computing]: Mathematical Software—Algorithm design and analysis; I.5.3 [Computing Methodologies]: Pattern Recognition—Clustering;
Conference_Titel :
Visual Analytics Science and Technology, 2008. VAST '08. IEEE Symposium on
Conference_Location :
Columbus, OH
Print_ISBN :
978-1-4244-2935-6
DOI :
10.1109/VAST.2008.4677367