Separation in Data Mining Based on Fractal Nature of Data
Result description
The separation of the searched data from the rest is an important task in data mining. Three separation/classification methods are presented. We use a singularity exponent in classifiers that are based on distances of patterns to a given (classified) pattern. The approximation of so called probability distribution mapping function of the distribution of points from the viewpoint of distances from a given point in the form of a scaling exponent power of a distance is presented together with a way how tostate it. Considering data as points in a metric space, three methods are based on transformed distances of neighbors of a given point in a multidimensional space via functions that use different estimates of scaling exponent. Classifiers ? data separators utilizing knowledge about explored data distribution in a space and suggested expressions of the scaling exponent are presented. Experimental results on both synthetic and real-life data show interesting behavior (classification accura
Keywords
nearest neighborfractal setmultifractalIINC methodcorrelation dimension
The result's identifiers
Result code in IS VaVaI
Result on the web
http://sdiwc.net/digital-library/separation-in-data-mining-based-on-fractal-nature-of-data.html
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Separation in Data Mining Based on Fractal Nature of Data
Original language description
The separation of the searched data from the rest is an important task in data mining. Three separation/classification methods are presented. We use a singularity exponent in classifiers that are based on distances of patterns to a given (classified) pattern. The approximation of so called probability distribution mapping function of the distribution of points from the viewpoint of distances from a given point in the form of a scaling exponent power of a distance is presented together with a way how tostate it. Considering data as points in a metric space, three methods are based on transformed distances of neighbors of a given point in a multidimensional space via functions that use different estimates of scaling exponent. Classifiers ? data separators utilizing knowledge about explored data distribution in a space and suggested expressions of the scaling exponent are presented. Experimental results on both synthetic and real-life data show interesting behavior (classification accura
Czech name
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Czech description
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Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Digital Information and Wireless Communications
ISSN
2225-658X
e-ISSN
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Volume of the periodical
3
Issue of the periodical within the volume
1
Country of publishing house
HK - HONG KONG
Number of pages
17
Pages from-to
44-60
UT code for WoS article
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EID of the result in the Scopus database
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Basic information
Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
JC - Computer hardware and software
Year of implementation
2013