Autor:

Suchy, Dawid ; Simiński, Krzysztof

Współtwórca:

Kitowski, Zygmunt - ed. ; Piskur, Paweł - ed. ; Hożyń , Stanisław - ed.

Tytuł:

GrDBSCAN: A granular density-based clustering algorithm

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 33 (2023)

Temat i słowa kluczowe:

granular computing ; DBSCAN ; clustering ; GrDBSCAN

Abstract:

Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback - its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. ; The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2023

Typ zasobu:

artykuł

DOI:

10.34768/amcs-2023-0022

Strony:

297-312

Źródło:

AMCS, volume 33, number 2 (2023) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego