Weighted clustering ensemble: A review
Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving
both the robustness and the stability of results from individual clustering methods. Weighted …
both the robustness and the stability of results from individual clustering methods. Weighted …
An improved density peaks clustering algorithm based on the generalized neighbors similarity
X Yang, F **ao - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Density peaks clustering (DPC) algorithm reported in Science is a novel and efficient
clustering method which has attracted great attention for its simplicity and practicability …
clustering method which has attracted great attention for its simplicity and practicability …
A new method for weighted ensemble clustering and coupled ensemble selection
Clustering ensemble, also referred to as consensus clustering, has emerged as a method of
combining an ensemble of different clusterings to derive a final clustering that is of better …
combining an ensemble of different clusterings to derive a final clustering that is of better …
Improving k-means through distributed scalable metaheuristics
The recent growing size of datasets requires scalability of data mining algorithms, such as
clustering algorithms. The MapReduce programing model provides the scalability needed …
clustering algorithms. The MapReduce programing model provides the scalability needed …
Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble
Clustering ensemble can overcome the instability of clustering and improve clustering
performance. With the rapid development of clustering ensemble, we find that not all …
performance. With the rapid development of clustering ensemble, we find that not all …
An effective multiobjective approach for hard partitional clustering
Clustering is an unsupervised classification method in the field of data mining. Many
population based evolutionary and swarm intelligence optimization methods are proposed …
population based evolutionary and swarm intelligence optimization methods are proposed …
Cohesive clustering algorithm based on high-dimensional generalized Fermat points
T Li, X Wang, H Zhong - Information Sciences, 2022 - Elsevier
Utilizing high-dimensional generalized Fermat points (F d-points) as cluster centers, we
propose a new method F d-points Linkage (FL) for calculating intra-cluster and inter-cluster …
propose a new method F d-points Linkage (FL) for calculating intra-cluster and inter-cluster …
Selecting the Number of Clusters K with a Stability Trade-off: An Internal Validation Criterion
Abstract Model selection is a major challenge in non-parametric clustering. There is no
universally admitted way to evaluate clustering results for the obvious reason that no ground …
universally admitted way to evaluate clustering results for the obvious reason that no ground …
Autonomous clustering by fast find of mass and distance peaks
Clustering is an essential analytical tool across a wide range of scientific fields, including
biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel …
biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel …
Local peaks-based clustering algorithm in symmetric neighborhood graph
Density-based clustering methods have achieved many applications in data mining,
whereas most of them still likely suffer poor performances on data sets with extremely …
whereas most of them still likely suffer poor performances on data sets with extremely …