Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …

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 …

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 …

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 …

A growing number of applications generate streaming data, making data stream mining a
popular research topic. Classification-based streaming algorithms require pre-training on …

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 …

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 …

Mining data containing density-based clusters is well-established and widespread but faces
problems when it comes to systematic and reproducible comparison and evaluation …

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 …

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 …