Local kernels based graph learning for multiple kernel clustering 2024, Pattern Recognition Citation Excerpt : As a widely applied technique to handle nonlinear data, kernel learning has been a hot research topi
Then its performance is evaluated on simulated data and compared to a multiple kernel integration method, and two real-world data sets are analyzed: the first one is a single-cell data set that illustrates the comparison of clusterings for different cell-types in mouse embryos. The second one...
Kernel-induced distance function between datasets was discussed and then kernelized hierarchical clustering was developed and used in determining the structure of decision tree. Further, simulation results on satellite image interpretation show the superiority of the proposed classification strategy over the ...
Simulation results show that it outperforms LEACH, having the problem of overhead and complexity in clusters formation in multiple levels, and implementation of the threshold based functions. 5.3.6 Hybrid energy-efficient distributed clustering (HEED) HEED (Younis and Fahmy, 2004) is an extension ...
Network architecture of ScLSTM. The improved sigmoid kernel is used to build the scRNA-seq feature matrix, which is then fed into the siamese LSTM. Using the agglomerative clustering algorithm, determine the cell type of the output of the siamese LSTM Full size image ...
The distribution also exhibited an enrichment at cluster boundaries, which indicates that the clustering is relevant with respect to the functional structure of the chromatin. Conclusions We have proposed an efficient approach to perform constrained hierarchical clustering based on kernel (or similarity) ...
Four machine learning modeling techniques, namely random forest (RF), least squares support vector machines (LS-SVM), simple boosted regression tree (SBRT), kernel extreme learning machine (K-ELM), and modified statistical models are used. With the help of hierarchical clustering on principal ...
Clustering methods divide a set of observations into groups in such a way that members of the same group are more similar to one another than to the members of the other groups. One of the scientifically well known methods of clustering is the hierarchical agglomerative one. For data of diffe...
In the first step, multiple clustering results are created, which is called the ensemble. And in the second step, the results from multiple clustering techniques are combined, using a consensus function which is called an aggregator [23], to create a single and integrated model for input ...
To examine whether the clustering is primarily driven by indivi- dual difference, we computed the proportion of 96 sliding windows (24 nonoverlapped windows × 4 runs) of each subject that were assigned to state 1, leaving the remaining windows assigned to state 2. We found that no individual...