Evidential evolving Gustafson–Kessel algorithm for online data streams partitioning using belief function theory

A new online clustering method called E2GK (Evidential Evolving Gustafson–Kessel) is introduced. This partitional clustering algorithm is based on the concept of credal partition defined in the theoretical framework of belief functions. A credal partition is derived online by applying an algorithm resulting from the adaptation of the Evolving Gustafson–Kessel (EGK) algorithm. Online partitioning of data streams is then possible with a meaningful interpretation of the data structure. A comparative study with the original online procedure shows that E2GK outperforms EGK on different entry data sets. To show the performance of E2GK, several experiments have been conducted on synthetic data sets as well as on data collected from a real application problem. A study of parameters’ sensitivity is also carried out and solutions are proposed to limit complexity issues.

► E2GK is a new online clustering method based on belief functions.
► E2GK brings meaningful interpretation of the data structure as data arrive.
► E2GK outperforms the Evolving Gustafson–Kessel algorithm (EGK).
► E2GK is more robust than EGK regarding critical parameters.
► Solutions are proposed to limit complexity issues of E2GK.