E2GK: Evidential Evolving Gustafsson-Kessel Algorithm for Data Streams Partitioning Using Belief Functions
نویسندگان
چکیده
A new online clustering method, called E2GK (Evidential Evolving Gustafson-Kessel) is introduced in the theoretical framework of belief functions. The algorithm enables an online partitioning of data streams based on two existing and efficient algorithms: Evidantial cMeans (ECM) and Evolving Gustafson-Kessel (EGK). E2GK uses the concept of credal partition of ECM and adapts EGK, offering a better interpretation of the data structure. Experiments with synthetic data sets show good performances of the proposed algorithm compared to the original online procedure.
منابع مشابه
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 partitionin...
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