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Objective: In this paper, we focused on devel-
oping a clustering approach for biological data. In many
biological analyses, such as multiomics data analysis and
genome-wide association studies analysis, it is crucial to
find groups of data belonging to subtypes of diseases or
tumors. Methods: Conventionally, the k-means clustering
algorithm is overwhelmingly applied in many areas includ-
ing biological sciences. There are, however, several alter-
native clustering algorithms that can be applied, including
support vector clustering. In this paper, taking into consid-
eration the nature of biological data, we propose a maxi-
mum likelihood clustering scheme based on a hierarchical
framework. Results: This method can perform clustering
even when the data belonging to different groups overlap.
It can also perform clustering when the number of samples
is lower than the data dimensionality. Conclusion: The pro-
posed scheme is free from selecting initial settings to begin
the search process. In addition, it does not require the com-
putation of the first and second derivative of likelihood func-
tions, as is required by many other maximum likelihood-
based methods. Significance: This algorithm uses distri-
bution and centroid information to cluster a sample and
was applied to biological data. A MATLAB implementa-
tion of this method can be downloaded from the web link
http://www.riken.jp/en/research/labs/ims/med_sci_math/
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