Christopher G. Healey - Summarization techniques for visualization of

Tech report

S. Kocherlakota, C. G. Healey
TR-2005-35, North Carolina State University, 2005

Cite

APA Click to copy
Kocherlakota, S., & Healey, C. G. (2005). Summarization techniques for visualization of large, multidimensional datasets. North Carolina State University.

Chicago/Turabian Click to copy
Kocherlakota, S., and C. G. Healey. Summarization Techniques for Visualization of Large, Multidimensional Datasets. North Carolina State University, 2005.

MLA Click to copy
Kocherlakota, S., and C. G. Healey. Summarization Techniques for Visualization of Large, Multidimensional Datasets. no. TR-2005-35, North Carolina State University, 2005.

BibTeX Click to copy

@techreport{s2005a,
  title = {Summarization techniques for visualization of large, multidimensional datasets},
  year = {2005},
  institution = {North Carolina State University},
  issue = {TR-2005-35},
  author = {Kocherlakota, S. and Healey, C. G.}
}

Abstract

One of the main issues confronting visualization, is how to effectively display large, high dimensional datasets within a limited display area, without overwhelming the user. In this report, we discuss a data summarization approach to tackle this problem. Summarization is the process by which data is reduced in a meaningful and intelligent fashion, to its important and relevant features. We survey several different techniques from within computer science, which can be used to extract various characteristics from raw data. Using summarization techniques intelligently within visualization systems, could potentially reduce the size and dimensionality of large, high dimensional data, highlight relevant and important features, and enhance comprehension.