Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an optimised model-free expectation maximisation 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 maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions are continued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as ll as real multi-colour flow cytometry datasets shod that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters or their distribution models.
['Başak Esin Köktürk', 'Bilge Karacali']
I am writing a research paper on a idea Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering
[{"from": "human", "value": "I am writing a research paper on a idea Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an optimised model-free expectation maximisation 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 maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions are continued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as ll as real multi-colour flow cytometry datasets shod that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters or their distribution models."}]