Köktürk, Başak EsinKaraçalı, Bilge2021-01-242021-01-242014978-1-4799-5669-22156-11252156-1133https://hdl.handle.net/11147/9962IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)This paper proposes a new method for automated clustering of high dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximization scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm. The divisions are carried out until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-color flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any knowledge on the number of clusters or their distribution models.eninfo:eu-repo/semantics/openAccessConference Object2-s2.0-84922779176