Rao DISC-based similarity coefficient: a measure of similarity with respect to feature differences

May Anne E. Mata

International Journal of Data Analysis Techniques and Strategies, 2010, vol. 2, issue 2, 181-198

Abstract: In this paper, I defined a similarity coefficient, called Rao DISC-based similarity coefficient (DbSC), which takes into consideration feature differences as a factor of measuring similarity. Such coefficient was based from Rao dissimilarity coefficient (DISC) and diversity coefficient (DIVC) which are mostly applicable to ecology. The performance of Rao DbSC was compared with the existing similarity coefficients using three different data sets. Principal coordinate analysis (PCoA) and Spearman's rank correlation were made to demonstrate how Rao DbSC differs from other existing similarity coefficients. The obtained results gave emphasis on the relevance of considering the differences among features when comparing samples. Generally, this paper has illustrated the possibility of taking feature differences through some notion of distance as basis for determining similarity between samples.

Keywords: data analysis; PCoA; Rao dissimilarity coefficient; DISC; Rao diversity coefficient; DIVC; quadratic entropy; similarity coefficients; feature differences; principal coordinate analysis. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=32457 (text/html)
Access to full text is restricted to subscribers.

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:ids:injdan:v:2:y:2010:i:2:p:181-198

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().