GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game
Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-
means is one of the well-known algorithms for clustering data. The foundation of this …
means is one of the well-known algorithms for clustering data. The foundation of this …
A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data
Clustering is an important technique to deal with large scale data which are explosively
created in internet. Most data are high-dimensional with a lot of noise, which brings great …
created in internet. Most data are high-dimensional with a lot of noise, which brings great …
Dual shared-specific multiview subspace clustering
Multiview subspace clustering has received significant attention as the availability of diverse
of multidomain and multiview real-world data has rapidly increased in the recent years …
of multidomain and multiview real-world data has rapidly increased in the recent years …
Survey and experimental study on metric learning methods
D Li, Y Tian - Neural networks, 2018 - Elsevier
Distance metric learning has been a hot research spot recently due to its high effectiveness
and efficiency in improving the performance of distance related methods, such as k nearest …
and efficiency in improving the performance of distance related methods, such as k nearest …
Structured general and specific multi-view subspace clustering
In this paper, we propose a structured general and specific multi-view subspace clustering
method for image clustering. Unlike most existing multi-view subspace clustering methods …
method for image clustering. Unlike most existing multi-view subspace clustering methods …
Impact identification of composite cylinder based on improved deep metric learning model and weighted fusion Tikhonov regularized total least squares
Composite materials are widely used in aerospace field due to the characteristics of light
weight and high strength. As the composites are prone to internal damage that is not visible …
weight and high strength. As the composites are prone to internal damage that is not visible …
Grid-based DBSCAN: Indexing and inference
DBSCAN is one of clustering algorithms which can report arbitrarily-shaped clusters and
noises without requiring the number of clusters as a parameter (unlike the other clustering …
noises without requiring the number of clusters as a parameter (unlike the other clustering …
Graph enhanced fuzzy clustering for categorical data using a Bayesian dissimilarity measure
C Zhang, L Chen, YP Zhao, Y Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Categorical data are widely available in many real-world applications, and to discover
valuable patterns in such data by clustering is of great importance. However, the lack of a …
valuable patterns in such data by clustering is of great importance. However, the lack of a …
[HTML][HTML] Una revisión sobre aprendizaje no supervisado de métricas de distancia
Muchos de los métodos de aprendizaje automático dependen del cálculo de distancias en
un espacio multidimensional para estimar la similitud entre dos ejemplos teniendo en …
un espacio multidimensional para estimar la similitud entre dos ejemplos teniendo en …
Inductive conformal predictor for convolutional neural networks: Applications to active learning for image classification
Conformal prediction uses the degree of strangeness (nonconformity) of data instances to
determine the confidence values of new predictions. We propose an inductive conformal …
determine the confidence values of new predictions. We propose an inductive conformal …