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Published in:
2011 | OriginalPaper | Chapter
E2GK: Evidential Evolving Gustafsson-Kessel Algorithm for Data Streams Partitioning Using Belief Functions
Authors : Lisa Serir, Emmanuel Ramasso, Noureddine Zerhouni
Published in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Publisher: Springer Berlin Heidelberg
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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
c
-Means (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.