- Tytuł:
- DSMK-means “density-based split-and-Merge K-means clustering algorithm
- Autorzy:
-
Aldahdooh, R. T.
Ashour, W. - Powiązania:
- https://bibliotekanauki.pl/articles/91719.pdf
- Data publikacji:
- 2013
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
clustering
K-means
Density-based Split
Merge K-means clustering Algorithm
DSMK-means
clustering algorithm - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 51-71
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł