Agrupamento Fuzzy para Fluxo Contínuo de Dados – Um Estudo de Algoritmos Baseados em Blocos - Asbahr, R. K.; Lopes, P. A.; Camargo, H. A. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2018: Anais do VI Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2018.27396, 2018)
Data Stream Mining (DSM) has become an important topic due to the increasing availability of large collections of data. These data sets are characterized by having potentially infinite size, which prevents them from being stored in their entirety, and can generate examples with changeable statistical distribution according to time. These characteristics impose the need to create and use appropriate algorithms. Clustering algorithms are appropriate for DSMs where the labeling of the examples is costly and time consuming. Fuzzy clustering algorithms present an additional benefit in these contexts by allowing decision surfaces to be defined flexibly. The objective of this work was to implement and analyze the behavior of chunk based fuzzy clustering algorithms for DSM. The experiments, using two synthetic datasets and one real data set, allow us to extract analyzes regarding trends in the behavior of the algorithms according to their abilities to treat two critical problems for this type of algorithm: change in the distribution of the data and definition of the number of groups.