DSMK-means “density-based split-and-Merge K-means clustering algorithm"

• Autorzy: Aldahdooh R. T. , Ashour W.

Identyfikatory

DOI

10.2478/jaiscr-2014-0005

• Języki publikacji: EN

Abstrakty

EN

Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split- and -Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise.

Słowa kluczowe

EN

clustering; K-means; Density-based Split; Merge K-means clustering Algorithm; DSMK-means; clustering algorithm

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2013
• Tom: Vol. 3, No. 1
• Strony: 51--71
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Aldahdooh R. T.

• Computer Engineering Dept., Islamic University of Gaza (IUG), Gaza, Palestine

autor

Ashour W.

• Computer Engineering Dept., Islamic University of Gaza (IUG), Gaza, Palestine

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