A survey on soft subspace clustering
Subspace clustering (SC) is a promising technology involving clusters that are identified
based on their association with subspaces in high-dimensional spaces. SC can be classified …
based on their association with subspaces in high-dimensional spaces. SC can be classified …
Collaborative fuzzy clustering from multiple weighted views
Clustering with multiview data is becoming a hot topic in data mining, pattern recognition,
and machine learning. In order to realize an effective multiview clustering, two issues must …
and machine learning. In order to realize an effective multiview clustering, two issues must …
A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data
Y Chen, S Tang, N Bouguila, C Wang, J Du, HL Li - Pattern Recognition, 2018 - Elsevier
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 …
Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19
P Singh, SS Bose - Knowledge-Based Systems, 2021 - Elsevier
Abstract Coronavirus Disease 2019 (COVID-19) has been considered one of the most
critical diseases of the 21st century. Only early detection can aid in the prevention of …
critical diseases of the 21st century. Only early detection can aid in the prevention of …
Fuzzy clustering with the entropy of attribute weights
For many datasets, it is a difficult work to seek a proper cluster structure which covers the
entire feature set. To extract the important features and improve the clustering, the maximum …
entire feature set. To extract the important features and improve the clustering, the maximum …
Dynamic clustering with improved binary artificial bee colony algorithm
One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is
the similarity measure based discrete artificial bee colony, which was first proposed to deal …
the similarity measure based discrete artificial bee colony, which was first proposed to deal …
TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data
This paper proposes TW-k-means, an automated two-level variable weighting clustering
algorithm for multiview data, which can simultaneously compute weights for views and …
algorithm for multiview data, which can simultaneously compute weights for views and …
Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking
T Wei, X Wang, X Li, S Zhu - Engineering Applications of Artificial …, 2022 - Elsevier
Abstract The Fuzzy C-means (FCM) clustering algorithm is an effective method for image
segmentation. Non-local spatial information considers more redundant information of the …
segmentation. Non-local spatial information considers more redundant information of the …
Exploring a rich spatial–temporal dependent relational model for skeleton-based action recognition by bidirectional LSTM-CNN
With the fast development of effective and low-cost human skeleton capture systems,
skeleton-based action recognition has attracted much attention recently. Most existing …
skeleton-based action recognition has attracted much attention recently. Most existing …
Partitive clustering (K‐means family)
Y **ao, J Yu - Wiley Interdisciplinary Reviews: Data Mining and …, 2012 - Wiley Online Library
Partitional clustering is an important part of cluster analysis. Cluster analysis can be
considered as one of the the most important approaches to unsupervised learning. The goal …
considered as one of the the most important approaches to unsupervised learning. The goal …