Fuzzy Clustering Algorithms

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.