An Evaluation of the Objective Clustering Inductive Technology Effectiveness Implemented Using Density-Based and Agglomerative Hierarchical Clustering Algorithms

Autor:	Bohdan Durnyak, Sergii Babichev, Vsevolod Senkivskyy, Iryna Pikh
Rok vydání:	2019
Předmět:	DBSCAN Density based Computer science Process (computing) Agglomerative hierarchical clustering High effectiveness Cluster analysis Algorithm Hierarchical clustering Hierarchical algorithm
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030264734 ISDMCI
DOI:	10.1007/978-3-030-26474-1_37
Popis:	The paper presents the results of the research concerning comparison analysis of the efectiveness of OPTICS and DBSCAN density-based and agglonarative hierarchical clustering algorithms within the framework of the objective clustering inductive technology. Implementation of this technology allows us to determine the optimal parameters of appropriate clustering algorithm in terms of the maximum values of the complex balance criterion which contains as the components both the internal and the external clustering quality criteria. The data from the Computing School of East Finland University database were used as the experimental one during the simulation process. The results of the simulation have shown high effectiveness of the proposed technique. The investigated objects were divided into clusters correctly in all cases. Moreover, the results of the simulation have shown also higher effectiveness of the density-based clustering algorithms in comparison with agglomerative hierarchical algorithm use due to more level of the detail during the objects clustering.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5fdee3ad244c808cc56c7331543e021d https://doi.org/10.1007/978-3-030-26474-1_37 Zobrazit plný text záznamu