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Abstract: Various hard, fuzzy and possibilistic clustering criteria (objective functions) are useful as bases for a variety of pattern recognition problems.
This note shows how to reformulate some clustering criteria so that specialized algorithms can be replaced by general optimization routines found in ...
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Various hard, fuzzy and possibilistic clustering criteria (objective functions) are useful as bases for a variety of pattern recognition problems.
Various hard, fuzzy and possibilistic clustering criteria (objective functions) are useful as bases for a variety of pattern recognition problems.
In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method ...
The problem of classifying objects into homogeneous clusters appears commonly in all sciences. In such studies, one also often needs simple but reliable ...
In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analysis. Our goal is to find an explanation for each ...
Jul 11, 2023 · This paper presents k-plus, an extension of the classical k-means objective of maximizing between-group similarity in anticlustering applications.
This paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization.
Aug 16, 2023 · We propose an efficient and scalable in-processing algorithm, driven by findings from the field of combinatorial optimization, that heuristically solves the ...