[HTML][HTML] A comprehensive survey of clustering algorithms
Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …
communication science, computer science and biology science. Clustering, as the basic …
Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery
Owing to the space–air–ground integrated networks (SAGIN), seaborne shipping has
attracted increasing interest in the research on the motion behavior knowledge extraction …
attracted increasing interest in the research on the motion behavior knowledge extraction …
[HTML][HTML] Deep Learning approaches for visual faults diagnosis of photovoltaic systems: State-of-the-art review
PV systems are prone to external environmental conditions that affect PV system operations.
Visual inspection of the impacts of faults on PV system is considered a better practice rather …
Visual inspection of the impacts of faults on PV system is considered a better practice rather …
Graph dual regularization non-negative matrix factorization for co-clustering
Low-rank matrix factorization is one of the most useful tools in scientific computing, data
mining and computer vision. Among of its techniques, non-negative matrix factorization …
mining and computer vision. Among of its techniques, non-negative matrix factorization …
Ensemble clustering using factor graph
In this paper, we propose a new ensemble clustering approach termed ensemble clustering
using factor graph (ECFG). Compared to the existing approaches, our approach has three …
using factor graph (ECFG). Compared to the existing approaches, our approach has three …
A novel AI-based framework for AoI-optimal trajectory planning in UAV-assisted wireless sensor networks
Information freshness, which is characterized by a new performance metric called age of
information (AoI), significantly influences decision making in numerous applications. In …
information (AoI), significantly influences decision making in numerous applications. In …
A multi-pattern deep fusion model for short-term bus passenger flow forecasting
Short-term passenger flow forecasting is one of the crucial components in transportation
systems with data support for transportation planning and management. For forecasting bus …
systems with data support for transportation planning and management. For forecasting bus …
Link based BPSO for feature selection in big data text clustering
N Kushwaha, M Pant - Future generation computer systems, 2018 - Elsevier
Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance …
which eliminates irrelevant and redundant features and improves learning performance …
Sok: Efficient privacy-preserving clustering
Clustering is a popular unsupervised machine learning technique that groups similar input
elements into clusters. It is used in many areas ranging from business analysis to health …
elements into clusters. It is used in many areas ranging from business analysis to health …
Dual graph-regularized sparse concept factorization for clustering
The concept factorization algorithm has received widespread attention and achieved
remarkable results in the field of clustering. However, when modeling this clustering …
remarkable results in the field of clustering. However, when modeling this clustering …