Comparison Analysis of Clustering Quality Criteria Using Inductive Methods of Objective Clustering

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F20%3A43895694" target="_blank" >RIV/44555601:13440/20:43895694 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/book/10.1007/978-3-030-61656-4" target="_blank" >https://link.springer.com/book/10.1007/978-3-030-61656-4</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Comparison Analysis of Clustering Quality Criteria Using Inductive Methods of Objective Clustering

Original language description

In this paper, we present the results of the research concerning comparison analysis of both the internal and external clustering quality criteria for clustering various types of datasets using density-based DBSCAN clustering algorithm implemented based on Inductive Methods of Objective Clustering (IMOC). Implementation of the IMOC technique assumes division of the initial dataset into two similar subsets contained the same number of pairwise similar objects at the first step of this procedure implementation. Then, we have executed the data clustering on the obtained subsets concurrently within the range of the appropriate algorithm parameters variation with estimation of various types of clustering quality criteria (internal (IQC) and external (EQC)) at each step of this procedure implementation. The final solution concerning algorithm optimal parameters determination was made based on the maximum values of the complex balance criterion (CBC) which contains both the ICQ and ECQ as the components. The analysis of the simulation results has allowed us to evaluate the effectiveness of both the internal and external clustering quality criteria to determine the optimal parameters of clustering algorithm using various type of data. To our mind, the obtained results can allow us to increase the clustering procedure exactness and to decrease the reproducibility error.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Article name in the collection

Communications in Computer and Information Science

ISBN

978-3-030-61655-7

ISSN

1865-0929

e-ISSN

1865-0937

Number of pages

17

Pages from-to

150-166

Publisher name

Springer Nature Switzerland AG

Place of publication

Switzerland

Event location

Lviv, Ukraine

Event date

Aug 21, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—