[ Algoritmos de agrupación difusos ]
Volume 24, Issue 1, August 2018, Pages 17–30
Gary Reyes Zambrano1 and Christopher Crespo León2
1 Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Guayaquil, Ecuador
2 Facultad de Ciencias Administrativas, Universidad de Guayaquil, Guayaquil, Ecuador
Original language: Spanish
Copyright © 2018 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In recent years advances in technology have led to the generation of large volumes of data, mainly numerical data, highlighting the interest in processing them to extract knowledge and information from them. The main objective is to make more efficient the systems from which these data have been obtained and help in decision making. The information in a database is implicit in the values that represent the different states of the systems, whereas the knowledge is implicit in the relations between the values of the different attributes or present characteristics. These relationships are identified by groups to be discovered and describe the relationships between the input and output states. One of the main human functions is to classify, differentiate and group different objects according to their attributes. The article investigates how to apply fuzzy grouping algorithms, which allow an element to belong to more than one group by a degree of membership, in order to obtain relevant characteristics or recognize patterns of a set of data. We discuss a study that involved 4 main fuzzy algorithms where each algorithm is explained and how they are related, as well as with each new algorithm solves problems that the previous one did not solve efficiently.
Author Keywords: Diffuse grouping, Fuzzy logic, Data mining and Diffuze technology.
Volume 24, Issue 1, August 2018, Pages 17–30
Gary Reyes Zambrano1 and Christopher Crespo León2
1 Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Guayaquil, Ecuador
2 Facultad de Ciencias Administrativas, Universidad de Guayaquil, Guayaquil, Ecuador
Original language: Spanish
Copyright © 2018 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In recent years advances in technology have led to the generation of large volumes of data, mainly numerical data, highlighting the interest in processing them to extract knowledge and information from them. The main objective is to make more efficient the systems from which these data have been obtained and help in decision making. The information in a database is implicit in the values that represent the different states of the systems, whereas the knowledge is implicit in the relations between the values of the different attributes or present characteristics. These relationships are identified by groups to be discovered and describe the relationships between the input and output states. One of the main human functions is to classify, differentiate and group different objects according to their attributes. The article investigates how to apply fuzzy grouping algorithms, which allow an element to belong to more than one group by a degree of membership, in order to obtain relevant characteristics or recognize patterns of a set of data. We discuss a study that involved 4 main fuzzy algorithms where each algorithm is explained and how they are related, as well as with each new algorithm solves problems that the previous one did not solve efficiently.
Author Keywords: Diffuse grouping, Fuzzy logic, Data mining and Diffuze technology.
Abstract: (spanish)
En los últimos años, los avances en la tecnología han llevado a la generación de grandes volúmenes de datos, principalmente datos numéricos, destacando el interés en procesarlos para extraer conocimiento e información de ellos. El objetivo principal es hacer más eficientes los sistemas a partir de los cuales se han obtenido estos datos y ayudar en la toma de decisiones. La información en una base de datos está implícita en los valores que representan los diferentes estados de los sistemas, mientras que el conocimiento está implícito en las relaciones entre los valores de los diferentes atributos o características presentes. Estas relaciones son identificadas por grupos para ser descubiertas y describen las relaciones entre los estados de entrada y salida. Una de las principales funciones humanas es clasificar, diferenciar y agrupar diferentes objetos según sus atributos. El artículo investiga cómo aplicar algoritmos de agrupamiento difusos, que permiten que un elemento pertenezca a más de un grupo por un grado de membresía, con el fin de obtener características relevantes o reconocer patrones de un conjunto de datos. Discutimos un estudio que involucró 4 algoritmos difusos principales donde cada algoritmo se explica y cómo se relacionan, así como con cada nuevo algoritmo resuelve problemas que el anterior no resolvió de manera eficiente.
Author Keywords: Agrupación difusa, lógica difusa, extracción de datos y tecnología Difsa.
How to Cite this Article
Gary Reyes Zambrano and Christopher Crespo León, “Fuzzy Clustering Algorithms,” International Journal of Innovation and Applied Studies, vol. 24, no. 1, pp. 17–30, August 2018.
