Enhancement of OPTICS’ time complexity by using fuzzy clusters

Autor:	Safaa O. Al-Mamory, Israa S. Kamil
Rok vydání:	2023
Předmět:	DBSCAN business.industry Computer science Process (computing) Structure (category theory) General Medicine ComputingMethodologies_PATTERNRECOGNITION Optics Cluster (physics) State space Noise (video) Cluster analysis business Time complexity
Zdroj:	Materials Today: Proceedings. 80:2625-2630
ISSN:	2214-7853
DOI:	10.1016/j.matpr.2021.06.441
Popis:	Density-Based clustering are the main clustering algorithms because they can cluster data with different shapes and densities, but some of these algorithms have high time complexity like OPTICS (Ordering Points to Identify Clustering Structure) and DBSCAN (Density-based spatial clustering of applications with noise) where the time complexity up to O( n 2 ). In this paper, we use an approach to reduce this complexity by providing fuzzy clusters to OPTICS, which make the process of finding neighbours within an only narrow region (fuzzy group) instead of searching all the state space making the algorithm faster than the original one with keeping almost the same accuracy of the innovative algorithm. The results show that there is an improvement in execution time using some synthetic and real datasets with high dimensions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bf232903040da43f84cecd75e0fd0dea https://doi.org/10.1016/j.matpr.2021.06.441 Zobrazit plný text záznamu Full Text from ScienceDirect