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Adaptive graph auto-encoder for general data clustering
Graph-based clustering plays an important role in the clustering area. Recent studies about
graph neural networks (GNN) have achieved impressive success on graph-type data …
graph neural networks (GNN) have achieved impressive success on graph-type data …
Sparse K-means clustering algorithm with anchor graph regularization
As a classical unsupervised learning method, the K-means algorithm selects the cluster
centers randomly and calculates the mean values of the cluster's data points to generate …
centers randomly and calculates the mean values of the cluster's data points to generate …
Deep fuzzy k-means with adaptive loss and entropy regularization
Neural network based clustering methods usually have better performance compared to the
conventional approaches due to more efficient feature extraction. Most of existing deep …
conventional approaches due to more efficient feature extraction. Most of existing deep …
Embedding graph auto-encoder for graph clustering
Graph clustering, aiming to partition nodes of a graph into various groups via an
unsupervised approach, is an attractive topic in recent years. To improve the representative …
unsupervised approach, is an attractive topic in recent years. To improve the representative …
Iteratively Reweighted Algorithm for Fuzzy -Means
Fuzzy-means method (FCM) is a popular clustering method, which uses alternating iteration
algorithm to update membership matrix and center matrix of size. However, original FCM …
algorithm to update membership matrix and center matrix of size. However, original FCM …
Improving projected fuzzy K-means clustering via robust learning
Fuzzy K-Means clustering has been an attractive research area for many multimedia tasks.
Due to the interference of the noise and outliers, the performance of fuzzy K-Means …
Due to the interference of the noise and outliers, the performance of fuzzy K-Means …
A Sample-Rebalanced Outlier-Rejected -Nearest Neighbor Regression Model for Short-Term Traffic Flow Forecasting
Short-term traffic flow forecasting is a fundamental and challenging task due to the stochastic
dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a …
dynamics of the traffic flow, which is often imbalanced and noisy. This paper presents a …
Robust deep fuzzy K-means clustering for image data
Image clustering is a difficult task with important application value in computer vision. The
key to this task is the quality of images features. Most of current clustering methods …
key to this task is the quality of images features. Most of current clustering methods …
FKMAWCW: categorical fuzzy k-modes clustering with automated attribute-weight and cluster-weight learning
The fuzzy k-modes (FKM) is a popular method for clustering categorical data. However, the
main problem of this algorithm is that it is very sensitive to the initialization of primary …
main problem of this algorithm is that it is very sensitive to the initialization of primary …
Fuzzy graph clustering
Spectral clustering is a group of graph-based clustering methods in which the columns of the
scaled cluster indicator matrix can be obtained by stacking the eigenvectors of the Laplacian …
scaled cluster indicator matrix can be obtained by stacking the eigenvectors of the Laplacian …