Late fusion incomplete multi-view clustering
Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete
views to improve clustering performance. Among various excellent solutions, multiple kernel …
views to improve clustering performance. Among various excellent solutions, multiple kernel …
Enhanced ensemble clustering via fast propagation of cluster-wise similarities
Ensemble clustering has been a popular research topic in data mining and machine
learning. Despite its significant progress in recent years, there are still two challenging …
learning. Despite its significant progress in recent years, there are still two challenging …
A discrete hidden Markov model fault diagnosis strategy based on K-means clustering dedicated to PEM fuel cell systems of tramways
J Liu, Q Li, W Chen, T Cao - International Journal of Hydrogen Energy, 2018 - Elsevier
To solve the fault classification problems of fuel cell (FC) various health states for tramways,
a discrete hidden Markov model (DHMM) fault diagnosis strategy based on K-means …
a discrete hidden Markov model (DHMM) fault diagnosis strategy based on K-means …
A semi-supervised approximate spectral clustering algorithm based on HMRF model
Before clustering, we usually have some background knowledge about the data structure.
Pairwise constraints are commonly used background knowledge. For graph partition …
Pairwise constraints are commonly used background knowledge. For graph partition …
Semi-supervised ensemble clustering based on selected constraint projection
Traditional cluster ensemble approaches have several limitations.(1) Few make use of prior
knowledge provided by experts.(2) It is difficult to achieve good performance in high …
knowledge provided by experts.(2) It is difficult to achieve good performance in high …
Structure-preserved unsupervised domain adaptation
Domain adaptation has been a primal approach to addressing the issues by lack of labels in
many data mining tasks. Although considerable efforts have been devoted to domain …
many data mining tasks. Although considerable efforts have been devoted to domain …
An Ensemble Clusterer of Multiple Fuzzy -Means Clusterings to Recognize Arbitrarily Shaped Clusters
Fuzzy cluster ensemble is an important research component of ensemble learning, which is
used to aggregate several fuzzy base clusterings to generate a single output clustering with …
used to aggregate several fuzzy base clusterings to generate a single output clustering with …
Sparse dual graph-regularized NMF for image co-clustering
Nonnegative matrix factorization (NMF) as fundamental technique for clustering has been
receiving more and more attention. This is because it can effectively reduce high …
receiving more and more attention. This is because it can effectively reduce high …
Infinite ensemble clustering
Ensemble clustering aims to fuse several diverse basic partitions into a consensus one,
which has been widely recognized as a promising tool to discover novel clusters and deliver …
which has been widely recognized as a promising tool to discover novel clusters and deliver …
Feature selection with unsupervised consensus guidance
Most of the unsupervised feature selection methods employ pseudo labels generated by
clustering to guide the feature selection; however, noisy and irrelevant features degrade the …
clustering to guide the feature selection; however, noisy and irrelevant features degrade the …