Therefore, we define these underlying structures as cluster units in this paper, then a unit-based consensus clustering algorithm is proposed based on the three-way decisions. The unit reflects a minimal granularity distribution structure agreed by all the ensemble members. Generally speaking, the ...
The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a visualization ...
We have created a stand-alone software tool, ConsensusCluster, for the analysis of high-dimensional single nucleotide polymorphism (SNP) and gene expression microarray data. Our software implements the consensus clustering algorithm and principal component analysis to stratify the data into a given numbe...
The method can also be used to represent the consensus over multiple runs of a clustering algorithm with random restart (such as K-means, model-based Bayesian clustering, SOM, etc.), so as to account for its sensitivity to the initial conditions. Finally, it provides for a vi- sualization...
Consensus clustering sudo code As the name says, consensus clustering gains a consensus on an observation’s cluster assignment based on their assignments in all the iterations of the clustering algorithm. We need to first decide how many iterations we wish to run. Next, we choose a set of K...
作者采用流形正则化(manifold regularization)重新表示原始特征,然后使用库恩-蒙克雷算法(Kuhn-Munkre algorithm)与共识聚类(consensus clustering)对多个隐藏层的聚类结果进行集成。 4.7.7 Query-By-Committee 委员会查询 委员会查询(Query-By-Committee)设计思想: 主动学习(Active Learning)是深度学习领域的另一个热门...
Clustering algorithm design: design the clustering algorithm according to the characteristics of the problem; (3) Result evaluation: evaluate the clustering result and judge the validity of algorithm; (4) Result explanation: give a practical explanation for the clustering result; ...
The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and ...
Results demonstrate that the consensus clustering and the elitist pareto-based multi-objective evolutionary algorithm can be effectively used in ensemble pruning. The experimental analysis with conventional ensemble methods and pruning algorithms indicates the validity and effectiveness of the proposed scheme....
MaxBin 2 is based on an expectation-maximization algorithm and uses tetranucleotides, differential coverage and marker genes13. MetaBAT applies a k-medoid clustering on tetranucleotide frequencies and differential coverage10. We also calculated tetranucleotide ESOMs4 and selected clusters manually using ...