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Ensemble clustering methods incorporate multiple base clusterings to provide a more accurate and reliable result compared to traditional clustering methods and have consequently gained popularity in recent years. In this paper, we propose a novel ...

Discovering knowledge from social media becomes a trend in many domains such as tourism, where users' feedback and rating are the basis of recommendation systems. In this context, cluster analysis has been a major tool to disclose user groups by ...

Ensemble clustering can obtain better and more robust results by fusing multiple base clusterings, which has received extensive attention. Although many representative algorithms have emerged in recent years, this field still has two tricky ...

• We propose a fast ensemble clustering algorithm based on spectral clustering.
• We propose a fast approximation method for K-nearest neighbors.
• We utilize anchor graphs to accelerate similarity graph construction.
• Our algorithm ...

It is commonly agreed that the quality of data analysis may be degraded by the presence of missing data. In various domains such as bioinformatics, an effective tool is required for the discovery of knowledge from gene expression datasets. One ...

• Novel extension of multiple-clustering to unsupervised KNN based imputation approach.
• A hybridisation with well-known techniques of ensemble clustering.
• ABC optimisation with two new operators is employ to select clusterings from a ...

Attack detection is one of the main features required in modern defence systems. Despite the ongoing research, it remains challenging for a typical mechanism like network-based intrusion detection system (NIDS) to catch up with evolving ...

Ensemble clustering integrates all basic clustering results to produce a better clustering result. Existing ensemble clustering methods typically rely on a co-association matrix (CA), which measures the number of occurrences two samples are ...

Clustering is a fundamental task in data analysis, aiming to partition a set of samples into subsets called clusters based on their similarity. Agglomerative Hierarchical Clustering (AHC) is a common hierarchical clustering method in which ...

• Presenting a robust semi-supervised method based on ensemble learning.
• Configuration of an AHC-based clustering framework by joining ECM and SSC.
• Introducing a new similarity measure based on a flexible weighting mechanism.
• ...

Ensemble clustering is an important problem in unsupervised learning that aims at aggregating multiple noisy partitions into a unique clustering solution. It can be formulated in terms of relabelling and voting, where relabelling refers to the ...

• Novel relabelling method for Ensemble Clustering based on permutation synchronization.
• Flexible formulation that can manage partitions with different numbers of clusters.
• Compares favourably against previous Ensemble Clustering ...

Manifold learning is essential for analyzing high-dimensional data, but it suffers from high time complexity. To address this, researchers proposed using anchors and constructing a similarity matrix to expedite eigen decomposition and reduce ...

• We propose a novel anchors generation method based on granular-ball computing.
• We present an ensemble clustering algorithm and it is more efficient than U-SPEC.
• The proposed algorithms can process millions of points on a commonly ...

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 ...

This paper addresses the critical gap in the understanding of the effects of various configurations and feedback mechanisms on the performance of hybrid metaheuristics (HMs) in unsupervised clustering applications. Despite the widespread use of ...

Ensemble clustering has attracted much attention for its robustness and effectiveness compared to single clustering. As one of the representative methods, most co-association matrix-based ensemble clustering typically only take into account a ...

Existing locally weighted ensemble clustering algorithms strive to weight each cluster and take into account the differences among all clusters, but they tend to ignore the basic cluster labels. The purpose of this paper is to combine the ...

Ensemble clustering is aimed at obtaining a robust consensus result from a set of weak base clusterings. Most existing methods rely on a co-association (CA) matrix that describes the frequency at which pairs of samples are clustered into the same ...

• A novel framework that fuses samples’ global and local structures is proposed for ensemble clustering.
• Two ensemble clustering models, FSEC-C and FSEC-Z are derived using the framework.
• L2 , 1-norm regularization is posed to ...

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 ...

Ensemble clustering algorithms have made significant progress in recent years due to their excellent performance. However, most of these algorithms face two challenges: one is to focus on the selection of subspaces since there is limited ...

• We propose a novel ensemble clustering framework.
• We define a diversified random metric space generated by the free exponential similarity kernel.
• We use the random walk strategy when weighting the collaborative incidence matrix.

Effectively managing cloud resources is a complex task due to the interdependencies of various cloud-hosted services and applications. This task is integral to workload categorization, which groups similar cloud workloads to inform scheduling and ...

As part of celebrating the success of OWA operators and their contributions over the past decades, this work presents an original investigation of exploiting OWA in dealing with missing value imputation witnessed in microarray experimental data. ...

• A novel extension of KNNImpute using ensemble clustering and optimisation.
• Two OWA aggregation strategies to summarise solutions from multiple clusterings.
• An empirical study on published gene expression dataset, using various ...

Graph-based methods have achieved great success in multi-view clustering. However, existing graph-based models generally utilize shallow and linear embedding functions to obtain the common spectral embedding for clustering assignments. In ...

• Our model integrates graph reconstruction, auto-encoder, ensemble clustering and contrastive learning into a unified framework to exploit the spectral embedding.
• The reconstructed graphs via our model own clear structures to reflect ...

Multi-view spectral clustering has achieved considerable performance in practice because of its ability to explore nonlinear structure information. However, most existing methods belong to shallow models and are sensitive to the original ...