A weighted framework for unsupervised ensemble learning based on internal quality measures
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Summary
Results on publicly available datasets show that weights can significantly improve the accuracy performance while retaining the robust properties and compare against other popular approaches.
- Type
- article
- Published
- 2017-11-21
- Cited by
- 22
- References
- 57
- OpenAlex
- https://openalex.org/W2768342049
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:109938343
Keywords
Cluster analysis, Computer science, Weighting, Theory of computation, Artificial intelligence
References
- A Review on Consensus Clustering Methods
- A New Evaluation Measure for Imbalanced Datasets
- Handling imbalanced datasets: A review
- A robust unsupervised consensus control chart pattern recognition framework
- Consensus Clustering on big data
- Handbook of Massive Data Sets
- Clustering validity assessment: finding the optimal partitioning of a data set
- Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems
- FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data
- Some thoughts on bacterial classification.
- Experimental Comparison of Cluster Ensemble Methods
- Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
- Neuro-fuzzy And Soft Computing - A Computational Approach To Learning And Machine Intelligence [Book Reviews]
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
- Lagrangian relaxation and pegging test for the clique partitioning problem
- Hierarchical clustering schemes
- A Survey of Clustering Ensemble Algorithms
- Elementary Linkage Analysis for Isolating Orthogonal and Oblique Types and Typal Relevancies
- Breast Cancer Diagnosis and Prognosis Via Linear Programming
Cited by
- Estimating the number of clusters in a dataset via consensus clustering
- A living environment prediction model using ensemble machine learning techniques based on quality of life index
- Weighted Clustering Ensemble: A Review
- Müşteri Odaklı Pazarlama Stratejileri için Veri Madenciliği Teknikleri Kapsamında Perakende Sektöründe Kümeleme Analizi Uygulaması
- Post-consensus analysis of group decision making processes by means of a graph theoretic and an association rules mining approach
- A reduced variance unsupervised ensemble learning algorithm based on modern portfolio theory
- Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing
- Incorporation of gene ontology in identification of protein interactions from biomedical corpus: a multi-modal approach
- scMelody: An Enhanced Consensus-Based Clustering Model for Single-Cell Methylation Data by Reconstructing Cell-to-Cell Similarity
- Selective clustering ensemble based on kappa and F-score
- Robust and accurate performance anomaly detection and prediction for cloud applications: a novel ensemble learning-based framework
- MetaWCE: Learning to Weight for Weighted Cluster Ensemble
- Active Ensemble Learning for Knowledge Graph Error Detection
- Parameter-free ensemble clustering with dynamic weighting mechanism
- Improved Outlier Detection for Failure Forecasting using Anomaly Score Threshold Optimization and Ensemble Methods
- Ranking and Combining Latent Structured Predictive Scores without Labeled Data
- The significance of Kappa and F-score in clustering ensemble: a comprehensive analysis
- Spectral ensemble clustering from graph reconstruction with auto-weighted cluster
- A Unified Approach for Ensemble Function and Threshold Optimization in Anomaly-Based Failure Forecasting
- The Significance of Kappa and F-score in Clustering Ensemble: A Comprehensive Analysis
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