Model of the Objective Clustering Inductive Technology of Gene Expression Profiles Based on SOTA and DBSCAN Clustering Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F18%3A43893027" target="_blank" >RIV/44555601:13440/18:43893027 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-70581-1_2" target="_blank" >http://dx.doi.org/10.1007/978-3-319-70581-1_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-70581-1_2" target="_blank" >10.1007/978-3-319-70581-1_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model of the Objective Clustering Inductive Technology of Gene Expression Profiles Based on SOTA and DBSCAN Clustering Algorithms

Popis výsledku v původním jazyce

The paper presents the hybrid model of the objective clustering inductive technology based on complex using of the self-organizing SOTA and the density DBSCAN clustering algorithms. The inductive methods of complex systems analysis were used as the basis to implement the objective clustering inductive technology of gene expression profiles. To estimate the clustering quality for equal power subsets (include the same quantity of pairwise similar objects) the complex multiplicative criterion was calculated as the combination of the Calinski-Harabasz criterion and WB-index. The external clustering quality criterion is calculated as the normalized difference of the internal clustering quality criteria for the equal power subsets. The final decision concerning the determination of the optimal parameters of the clustering algorithm operation is done based on the maximum value of the Harrington desirability function that takes into account both the character of the objects and the clusters distribution in various clustering and the difference between clustering, which are implemented on the equal power subsets. The studied data grouping within the framework of the objective clustering inductive technology was performed in two stages. Firstly, the studied gene expression profiles were grouped with the use DBSCAN clustering algorithm. Then, the obtained set of gene expression profiles was divided into two clusters using SOTA clustering algorithm. This step-by-step procedure of the data clustering crates the conditions to save more useful information for following data processing.

Název v anglickém jazyce

Model of the Objective Clustering Inductive Technology of Gene Expression Profiles Based on SOTA and DBSCAN Clustering Algorithms

Popis výsledku anglicky

The paper presents the hybrid model of the objective clustering inductive technology based on complex using of the self-organizing SOTA and the density DBSCAN clustering algorithms. The inductive methods of complex systems analysis were used as the basis to implement the objective clustering inductive technology of gene expression profiles. To estimate the clustering quality for equal power subsets (include the same quantity of pairwise similar objects) the complex multiplicative criterion was calculated as the combination of the Calinski-Harabasz criterion and WB-index. The external clustering quality criterion is calculated as the normalized difference of the internal clustering quality criteria for the equal power subsets. The final decision concerning the determination of the optimal parameters of the clustering algorithm operation is done based on the maximum value of the Harrington desirability function that takes into account both the character of the objects and the clusters distribution in various clustering and the difference between clustering, which are implemented on the equal power subsets. The studied data grouping within the framework of the objective clustering inductive technology was performed in two stages. Firstly, the studied gene expression profiles were grouped with the use DBSCAN clustering algorithm. Then, the obtained set of gene expression profiles was divided into two clusters using SOTA clustering algorithm. This step-by-step procedure of the data clustering crates the conditions to save more useful information for following data processing.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing

ISBN

978-3-319-70580-4

ISSN

2194-5357

e-ISSN

neuvedeno

Počet stran výsledku

19

Strana od-do

21-39

Název nakladatele

Springer, Cham

Místo vydání

Springer International Publishing

Místo konání akce

Lviv, Ukraine

Datum konání akce

5. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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