Separation in Data Mining Based on Fractal Nature of Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00393276" target="_blank" >RIV/67985807:_____/13:00393276 - isvavai.cz</a>

Výsledek na webu

<a href="http://sdiwc.net/digital-library/separation-in-data-mining-based-on-fractal-nature-of-data.html" target="_blank" >http://sdiwc.net/digital-library/separation-in-data-mining-based-on-fractal-nature-of-data.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Separation in Data Mining Based on Fractal Nature of Data

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Separation in Data Mining Based on Fractal Nature of Data

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Digital Information and Wireless Communications

ISSN

2225-658X

e-ISSN

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Svazek periodika

3

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

HK - Hongkong

Počet stran výsledku

17

Strana od-do

44-60

Kód UT WoS článku

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EID výsledku v databázi Scopus

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