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Community detection in networks: A multidisciplinary review
The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …
complex real-world systems. One of the most important features in these networks is the …
Density peak clustering algorithms: A review on the decade 2014–2023
Density peak clustering (DPC) algorithm has become a well-known clustering method
during the last decade, The research communities believe that DPC is a powerful tool …
during the last decade, The research communities believe that DPC is a powerful tool …
Survey of state-of-the-art mixed data clustering algorithms
Mixed data comprises both numeric and categorical features, and mixed datasets occur
frequently in many domains, such as health, finance, and marketing. Clustering is often …
frequently in many domains, such as health, finance, and marketing. Clustering is often …
Dynamic graph-based label propagation for density peaks clustering
Clustering is a major approach in data mining and machine learning and has been
successful in many real-world applications. Density peaks clustering (DPC) is a recently …
successful in many real-world applications. Density peaks clustering (DPC) is a recently …
EDMD: An Entropy based Dissimilarity measure to cluster Mixed-categorical Data
The effectiveness of clustering techniques is significantly influenced by proximity measures
irrespective of type of data and categorical data is no exception. Most of the existing …
irrespective of type of data and categorical data is no exception. Most of the existing …
[HTML][HTML] Fast and general density peaks clustering
Density peaks is a popular clustering algorithm, used for many different applications,
especially for non-spherical data. Although powerful, its use is limited by quadratic time …
especially for non-spherical data. Although powerful, its use is limited by quadratic time …
A new adaptive mixture distance-based improved density peaks clustering for gearbox fault diagnosis
With the rapid development of sensors and mechanical systems, we produce an
exponentially large amount of data daily. Usually, faults are prevalent in these sensory …
exponentially large amount of data daily. Usually, faults are prevalent in these sensory …
Comparative density peaks clustering
Clustering analysis is one of the major topics in unsupervised machine learning. A recent
study proposes a novel density-based clustering algorithm called the Density Peaks. It is …
study proposes a novel density-based clustering algorithm called the Density Peaks. It is …
A hybrid k-prototypes clustering approach with improved sine-cosine algorithm for mixed-data classification
When dealing a classification problem with mixed data, most of conventional supervised
learning algorithms cannot perform well due to their numerical characteristics. However …
learning algorithms cannot perform well due to their numerical characteristics. However …
An efficient entropy based dissimilarity measure to cluster categorical data
Clustering is an unsupervised learning technique that discovers intrinsic groups based on
proximity between data points. Therefore, the performance of clustering techniques mainly …
proximity between data points. Therefore, the performance of clustering techniques mainly …