Review of clustering methods for functional data
Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …
continuous functions underlying the discrete measurements/observations. Application of …
Fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging
C Li, S Ding, X Xu, H Hou, L Ding - Information Sciences, 2023 - Elsevier
Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select
centers and effectively process complex data. However, it also has low operational efficiency …
centers and effectively process complex data. However, it also has low operational efficiency …
Connecting the Dots--Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering
Despite the popularity of density-based clustering, its procedural definition makes it difficult
to analyze compared to clustering methods that minimize a loss function. In this paper, we …
to analyze compared to clustering methods that minimize a loss function. In this paper, we …
A theoretical analysis of density peaks clustering and the component-wise peak-finding algorithm
Density peaks clustering detects modes as points with high density and large distance to
points of higher density. Each non-mode point is assigned to the same cluster as its nearest …
points of higher density. Each non-mode point is assigned to the same cluster as its nearest …
Efficient online stream clustering based on fast peeling of boundary micro-cluster
A growing number of applications generate streaming data, making data stream mining a
popular research topic. Classification-based streaming algorithms require pre-training on …
popular research topic. Classification-based streaming algorithms require pre-training on …
Introduction of artificial Intelligence
Artificial intelligence (AI) is referred to as the intelligence developed by machines with
mathematical modeling. In particular, AI is manifested by machine's ability to effectively …
mathematical modeling. In particular, AI is manifested by machine's ability to effectively …
Density peaks clustering based on Gaussian fuzzy neighborhood with noise parameter
Density peak clustering (DPC) is an effective clustering method known for its robustness,
non-iterative nature, and hybrid approach. However, it is not without limitations:(a) the …
non-iterative nature, and hybrid approach. However, it is not without limitations:(a) the …
Data with Density-Based Clusters: A Generator for Systematic Evaluation of Clustering Algorithms
Mining data containing density-based clusters is well-established and widespread but faces
problems when it comes to systematic and reproducible comparison and evaluation …
problems when it comes to systematic and reproducible comparison and evaluation …
Regularized semi-supervised KLFDA algorithm based on density peak clustering
X Tao, Y Bao, X Zhang, T Liang, L Qi, Z Fan… - Neural Computing and …, 2022 - Springer
To solve the problem that the existing semi-supervised FISHER discriminant analysis
algorithm (FDA) cannot effectively use both labeled and unlabeled data for learning, we …
algorithm (FDA) cannot effectively use both labeled and unlabeled data for learning, we …
Horizontal Federated Density Peaks Clustering
S Ding, C Li, X Xu, L Guo, L Ding… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Density peaks clustering (DPC) is a popular clustering algorithm, which has been studied
and favored by many scholars because of its simplicity, fewer parameters, and no iteration …
and favored by many scholars because of its simplicity, fewer parameters, and no iteration …