Symbolic data analysis: what is it?

Lynne Billard
Additional contact information
Lynne Billard: University of Georgia, Department of Statistics

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 261-269 from Springer

Abstract: Abstract Classical data values are single points in p-dimensional space; symbolic data values are hypercubes (broadly defined) in p-dimensional space (and/or a cartesian product of p distributions). While some datasets, be they small or large in size, naturally consist of symbolic data, many symbolic datasets result from the aggregation of large or extremely large classical datasets into smaller more managably sized datasets, with the aggregation criteria typically grounded on basic scientific questions of interest. Unlike classical data, symbolic data have internal variation and structure which must be taken into account when analysing the dataset. In this paper, we review briefly types of symbolic data, how they might be analysed and how such analysis differs from a traditional classical analysis.

Keywords: Lists; intervals and histogram data; variations; structures; comparison classical and symbolic data (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_20

Ordering information: This item can be ordered from
http://www.springer.com/9783790817096

DOI: 10.1007/978-3-7908-1709-6_20

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().