Estudio de estrategias para la aceleración de la convergencia del clustering mediante fuzzy c-means
En el presente trabajo de grado se lleva a cabo el estudio de estrategias para acelerar la convergencia del clustering empleando Fuzzy C-means (FCM), con la finalidad de encontrar la mejor variante del algoritmo de clustering FCM que permita reducir el tiempo de cómputo, coste computacional y calida...
- Autores:
-
Trujillo Ortiz, Diana Carolina
Sacristan Hernandez, Carlos Serjeif
- Tipo de recurso:
- http://purl.org/coar/version/c_b1a7d7d4d402bcce
- Fecha de publicación:
- 2016
- Institución:
- Universidad Industrial de Santander
- Repositorio:
- Repositorio UIS
- Idioma:
- spa
- OAI Identifier:
- oai:noesis.uis.edu.co:20.500.14071/35068
- Palabra clave:
- Cluster
Clustering
Fuzzy C-Means (Fcm)
Partición Difusa
Centroides
Convergencia.
In the present work
we perform a comparative study of strategies to accelerate the convergence of clustering using Fuzzy C-means (FCM)
in order to find the best implementation of the FCM clustering algorithm in terms of computational cost. The studied FCM variants related to the acceleration of clustering were Online Fuzzy C-means (OFCM)
and Single Pass Fuzzy C-means (SPFCM)
and Ramdom Sampling plus Extension Fuzzy C-means (RSEFCM) which are compared against the original FCM algorithm using three different databases in order to collect the necessary information about each clustering algorithm. I our experiments for assessing the speed up with respect to the classic FCM
we found that RSEFCM had the highest performance. With respect to the quality of the obtained partitions
RSEFCM yielded the more accurate and robust results among the studied versions of FCM. As for the complexity of the algorithms
experimental tests showed a linear behavior in the computational cost.
- Rights
- License
- Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)