A fast granular-ball-based density peaks clustering algorithm for large-scale data
D Cheng, Y Li, S Xia, G Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Density peaks clustering algorithm (DP) has difficulty in clustering large-scale data, because
it requires the distance matrix to compute the density and-distance for each object, which …
it requires the distance matrix to compute the density and-distance for each object, which …
Improved k-means clustering algorithm for big data based on distributed smartphoneneural engine processor
Clustering is one of the most significant applications in the big data field. However, using the
clustering technique with big data requires an ample amount of processing power and …
clustering technique with big data requires an ample amount of processing power and …
The study of generic model set for reducing calibration time in P300-based brain–computer interface
P300-based brain-computer interfaces (BCIs) provide an additional communication channel
for individuals with communication disabilities. In general, P300-based BCIs need to be …
for individuals with communication disabilities. In general, P300-based BCIs need to be …
K-means clustering with natural density peaks for discovering arbitrary-shaped clusters
D Cheng, J Huang, S Zhang, S Xia… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Due to simplicity, K-means has become a widely used clustering method. However, its
clustering result is seriously affected by the initial centers and the allocation strategy makes …
clustering result is seriously affected by the initial centers and the allocation strategy makes …
Multiple correlated attributes based physical layer authentication in wireless networks
Physical (PHY) layer authentication has been a significant trend towards ensuring the
identity security of terminals in wireless networks due to the high security and low …
identity security of terminals in wireless networks due to the high security and low …
Adaptive intrusion detection via GA-GOGMM-based pattern learning with fuzzy rough set-based attribute selection
In this paper, an adaptive network intrusion detection method using fuzzy rough set-based
feature selection and GA-GOGMM-based pattern learning is presented. Based on the fuzzy …
feature selection and GA-GOGMM-based pattern learning is presented. Based on the fuzzy …
Fast vehicle routing via knowledge transfer in a reproducing kernel Hilbert space
Vehicle routing problems (VRPs) are essential in logistics. In the literature, many exact and
heuristic optimization algorithms have been proposed to solve the VRPs. These traditional …
heuristic optimization algorithms have been proposed to solve the VRPs. These traditional …
High-density cluster core-based k-means clustering with an unknown number of clusters
The k-means algorithm, known for its simplicity and adaptability, faces challenges related to
manual cluster number selection and sensitivity to initial centroid placement. This paper …
manual cluster number selection and sensitivity to initial centroid placement. This paper …
Flexible subspace clustering: A joint feature selection and k-means clustering framework
Regarding as an important computing paradigm, cloud computing is to address big and
distributed databases and rather simple computation. In this paradigm, data mining is one of …
distributed databases and rather simple computation. In this paradigm, data mining is one of …
Deep fuzzy variable C-means clustering incorporated with curriculum learning
End-to-end deep clustering method utilizes deep neural networks to jointly learn
representation features and clustering assignments. Although many k-means-friendly deep …
representation features and clustering assignments. Although many k-means-friendly deep …