Estimation of the inductive model od objects clustering stability based on the K-means algorithm for different levels of data noise

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F16%3A43888048" target="_blank" >RIV/44555601:13440/16:43888048 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.15588/1607-3274-2016-4-7" target="_blank" >http://dx.doi.org/10.15588/1607-3274-2016-4-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15588/1607-3274-2016-4-7" target="_blank" >10.15588/1607-3274-2016-4-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimation of the inductive model od objects clustering stability based on the K-means algorithm for different levels of data noise

Popis výsledku v původním jazyce

The inductive model of the objective clustering of objects based on the k-means algorithm clustering is presented in the paper. The algorithm for division of initial data into two equal power subsets is proposed and practically implemented. The difference between the mass centres of the appropriate clusters in different clustering is proposed to use as an external balance criterion. Approbation of the proposed model operation was carried out using the data &quot;Compound&quot; and &quot;Aggregation&quot; of the database of the Computing School in the Eastern Finland University. The researches on the estimation of the model stability to a noise component using the data &quot;Seeds&quot; are presented in the paper. The algorithms k-means, c-means, inductive k-means and agglomerative hierarchical algorithm were used to compare the results of the experiment. The ways of further improvement of the proposed model in order to increase the objectivity of investigated data clustering were defined by the results of the simulation

Název v anglickém jazyce

Estimation of the inductive model od objects clustering stability based on the K-means algorithm for different levels of data noise

Popis výsledku anglicky

The inductive model of the objective clustering of objects based on the k-means algorithm clustering is presented in the paper. The algorithm for division of initial data into two equal power subsets is proposed and practically implemented. The difference between the mass centres of the appropriate clusters in different clustering is proposed to use as an external balance criterion. Approbation of the proposed model operation was carried out using the data &quot;Compound&quot; and &quot;Aggregation&quot; of the database of the Computing School in the Eastern Finland University. The researches on the estimation of the model stability to a noise component using the data &quot;Seeds&quot; are presented in the paper. The algorithms k-means, c-means, inductive k-means and agglomerative hierarchical algorithm were used to compare the results of the experiment. The ways of further improvement of the proposed model in order to increase the objectivity of investigated data clustering were defined by the results of the simulation

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Radio Electronics, Computer Science, Control

ISSN

1607-3274

e-ISSN

—

Svazek periodika

2016

Číslo periodika v rámci svazku

?4(39)

Stát vydavatele periodika

UA - Ukrajina

Počet stran výsledku

7

Strana od-do

54-60

Kód UT WoS článku

000393190800007

EID výsledku v databázi Scopus

—