Ensemble clustering refers to learning a robust and accurate consensus result from a collection of base clustering results. Despite extensive research on this topic, it remains challenging due to the absence of raw features and real labels of ...

Feature subset selection problem is an NP hard problem and there is a need for computationally efficient algorithms that find near optimal feature subsets which improve the performance of a classifier. Two major challenges for feature subset ...

The co-association (CA) matrix that describes connection relationship between instances is of importance for ensemble clustering. Existing ensemble clustering methods demonstrate that Laplacian matrix can help to improve the quality of CA matrix ...

• Kullback–Leibler divergence weights are introduced to CA matrix.
• A multi-order Laplacian matrix is embedded to the objective function of ensemble clustering.
• The optimal Laplacian matrix is learned by ADMM.
• Experimental results ...

Consensus clustering is gaining increasing attention for its high quality and robustness. In particular, k-means-based Consensus Clustering (KCC) converts the usual computationally expensive problem to a classic k-means clustering with generalized utility ...

Epigenetic modifications are implicated in the onset and progression of obstructive sleep apnea (OSA) and its complications through their bidirectional relationship with long-term chronic intermittent hypoxia (IH). However, the exact ...

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• Machine learning revealed a key role for acetylation modifications in OSA.
• ...

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus ...

In this paper, we focus on unsupervised representation learning for clustering of images. Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through ...

• Unsupervised RVFL (usRVFL) framework based on manifold regularization proposed to perform unsupervised representation learning.
• Embedded features allow usRVFL to perform unsupervised tasks such as clustering.
• Inspired by consensus ...

Random Vector Functional Link (RVFL) Networks have received a lot of attention due to the fast training speed as the non-iterative solution characteristic. Currently, the main research direction of RVFLs has supervised learning, including semi-...

Multiple imputation (MI) is a popular method for dealing with missing values. However, the suitable way for applying clustering after MI remains unclear: how to pool partitions? How to assess the clustering instability when data are incomplete? By ...

Recent advances in single-cell RNA sequencing(scRNA-seq) provide the possibility to allow researchers study cellular differentiation and heterogeneity at the single-cell level. Analysis of single-cell gene expression data taken during cell ...

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• A Bayesian tensor factorization-based framework was developed for cancer subtyping.

Breast cancer is a highly heterogeneous disease. Subtyping the disease and identifying the genomic features driving these subtypes are critical for precision oncology for breast cancer. This study focuses on developing a new ...

COVID-19 is a viral infection that affects people differently, where the majority of cases develop mild symptoms, some people require hospitalization, and unfortunately, a small number of patients perish. Hence, identifying risk ...

• PAM clustering with consensus mapping for the unsupervised risk-profile discovery of COVID-19 subjects.

The ensemble clustering tries to aggregate a number of basic clusterings with the aim of producing a more consistent, robust and well-performing consensus clustering result. The current paper wants to introduce an ensemble clustering method. The ...

Consensus clustering aims to combine multiple base clusters into a probably better and more robust clustering result. Despite the significant progress in recent years, the existing consensus clustering approaches are mostly designed for general-...

• Attribute-noise-induced ensemble generation is proposed for consensus clustering.

Because of the negative perception towards noise, it is commonly eliminated in the process of data cleansing prior to the analysis process. Some studies attempt to employ tolerant or robust algorithms to achieve a reliable outcome. One ...

Consensus clustering is a useful tool for robust or distributed clustering applications. However, given the fact that time complexities of the consensus functions scale linearly or quadratically with the number of combined clusterings, ...

The class imbalance problem is widely studied in the machine learning community, it is present in many real world applications such as spam filtering, anomaly detection and medical diagnosis. In this paper, we propose an adaptive fuzzy c-means based ...

In the real world, entities are often connected via multiple relations, forming multi-relational network. These complex networks need novel models for their representation and sophisticated tools for their analysis. Community detection is one of ...

Consensus clustering has been recently applied as a solution to the clustering problem. This combines multiple clusterings of a set of objects into a single integrated clustering. Consensus clustering algorithms attempt to find stable ...

• The proposed algorithm is able to find the number of original clusters most of the time.

Evolutionary K-Means (EKM), which combines K-Means and genetic algorithm, solves K-Means' initiation problem by selecting parameters automatically through the evolution of partitions. Currently, EKM algorithms usually choose silhouette index as cluster ...