Abstract
The popular Iris benchmark set is used to introduce the basic concepts of data analysis. Data scales (nominal, ordinal, interval, ratio) must be accounted for because certain mathematical operations are only appropriate for specific scales. Numerical data can be represented by sets, vectors, or matrices. Data analysis is often based on dissimilarity measures (like inner product norms, Lebesgue/Minkowski norms) or on similarity measures (like cosine, overlap, Dice, Jaccard, Tanimoto). Sequences can be analyzed using sequence relations (like Hamming, Levenshtein, edit distance). Data can be extracted from continuous signals by sampling and quantization. The Nyquist condition allows sampling without loss of information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
E. Anderson. The Irises of the Gaspe Peninsula. Bull. of the American Iris Society, 59:2–5, 1935.
J. C. Bezdek, J. M. Keller, R. Krishnapuram, L. I. Kuncheva, and N. R. Pal. Will the real iris data please stand up? IEEE Transactions on Fuzzy Systems, 7(3):368–369, 1999.
M. Blum, R. W. Floyd, V. Pratt, R. Rivest, and R. Tarjan. Time bounds for selection. Journal of Computer and System Sciences, 7:488–461, 1973.
R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179–188, 1936.
R. W. Hamming. Error detecting and error correcting codes. The Bell System Technical Journal, 26(2):147–160, April 1950.
V. I. Levenshtein. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10(8):707–710, 1966.
S. S. Stevens. On the theory of scales of measurement. Science, 103(2684):677–680, 1946.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden
About this chapter
Cite this chapter
Runkler, T. (2012). Data and Relations. In: Data Analytics. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-8348-2589-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-8348-2589-6_2
Published:
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
Print ISBN: 978-3-8348-2588-9
Online ISBN: 978-3-8348-2589-6
eBook Packages: Computer ScienceComputer Science (R0)