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Transforming complex problems into K-means solutions
K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …
A survey on information bottleneck
This survey is for the remembrance of one of the creators of the information bottleneck
theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information …
theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information …
Semi-supervised constrained clustering: An in-depth overview, ranked taxonomy and future research directions
G González-Almagro, D Peralta, E De Poorter… - ar** discrete sets of instances with similar characteristics. Constrained …
Comparative density peaks clustering
Clustering analysis is one of the major topics in unsupervised machine learning. A recent
study proposes a novel density-based clustering algorithm called the Density Peaks. It is …
study proposes a novel density-based clustering algorithm called the Density Peaks. It is …
An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering
A reliable nuclei segmentation is still an open-ended problem, especially in the breast
cancer histology images. For the same, this paper proposes an intelligent gravitational …
cancer histology images. For the same, this paper proposes an intelligent gravitational …
Pharmacoprint: A combination of a pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design
Structural fingerprints and pharmacophore modeling are methodologies that have been
used for at least 2 decades in various fields of cheminformatics, from similarity searching to …
used for at least 2 decades in various fields of cheminformatics, from similarity searching to …
A dynamic hierarchical incremental learning-based supervised clustering for data stream with considering concept drift
Clustering analysis is an important data mining method for data stream. Data stream
clustering is a branch of clustering in which the patterns are processed in an ordered …
clustering is a branch of clustering in which the patterns are processed in an ordered …
Generate pairwise constraints from unlabeled data for semi-supervised clustering
Pairwise constraint selection methods often rely on the label information of data to generate
pairwise constraints. This paper proposes a new method of selecting pairwise constraints …
pairwise constraints. This paper proposes a new method of selecting pairwise constraints …
Semi-supervised discriminative clustering with graph regularization
Pairwise constraints are a typical form of class information used in semi-supervised
clustering. Although various methods were proposed to combine unlabeled data with …
clustering. Although various methods were proposed to combine unlabeled data with …
Explanation guided cross-modal social image clustering
The integration of visual and semantic information has been found to play a role in
increasing the accuracy of social image clustering methods. However, existing approaches …
increasing the accuracy of social image clustering methods. However, existing approaches …