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The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
The k-means clustering algorithm is considered one of the most powerful and popular data
mining algorithms in the research community. However, despite its popularity, the algorithm …
mining algorithms in the research community. However, despite its popularity, the algorithm …
[HTML][HTML] An overview of clustering methods with guidelines for application in mental health research
Cluster analyzes have been widely used in mental health research to decompose inter-
individual heterogeneity by identifying more homogeneous subgroups of individuals …
individual heterogeneity by identifying more homogeneous subgroups of individuals …
[HTML][HTML] Maritime traffic partitioning: An adaptive semi-supervised spectral regularization approach for leveraging multi-graph evolutionary traffic interactions
Maritime situational awareness (MSA) has long been a critical focus within the domain of
maritime traffic surveillance and management. The increasing complexities of ship traffic …
maritime traffic surveillance and management. The increasing complexities of ship traffic …
iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate
understanding of the sequence (-structure)-function paradigm for DNAs, RNAs and proteins …
understanding of the sequence (-structure)-function paradigm for DNAs, RNAs and proteins …
Clustering algorithms: A comparative approach
Many real-world systems can be studied in terms of pattern recognition tasks, so that proper
use (and understanding) of machine learning methods in practical applications becomes …
use (and understanding) of machine learning methods in practical applications becomes …
A comprehensive survey of clustering algorithms
Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …
communication science, computer science and biology science. Clustering, as the basic …
Multi-view low-rank sparse subspace clustering
Most existing approaches address multi-view subspace clustering problem by constructing
the affinity matrix on each view separately and afterwards propose how to extend spectral …
the affinity matrix on each view separately and afterwards propose how to extend spectral …
k-shape: Efficient and accurate clustering of time series
The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …
substantial interest in the analysis and mining of time series. Clustering is one of the most …
Efficient kNN classification algorithm for big data
K nearest neighbors (kNN) is an efficient lazy learning algorithm and has successfully been
developed in real applications. It is natural to scale the kNN method to the large scale …
developed in real applications. It is natural to scale the kNN method to the large scale …
Rank-constrained spectral clustering with flexible embedding
Spectral clustering (SC) has been proven to be effective in various applications. However,
the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed …
the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed …