Adaptive clustering algorithm based on kNN and density
B Shi, L Han, H Yan - Pattern Recognition Letters, 2018 - Elsevier
Although many clustering algorithms have been proposed, they all have various limitations.
Existing clustering algorithms usually require the user to set the appropriate threshold …
Existing clustering algorithms usually require the user to set the appropriate threshold …
DPC-LG: Density peaks clustering based on logistic distribution and gravitation
Abstract The Density Peaks Clustering (DPC) algorithm, published in Science, is a novel
density-based clustering approach. Gravitation-based Density Peaks Clustering (GDPC) …
density-based clustering approach. Gravitation-based Density Peaks Clustering (GDPC) …
Improved fruit fly optimization algorithm-based density peak clustering and its applications
Sažetak As density-based algorithm, Density Peak Clustering (DPC) algorithm has
superiority of clustering by finding the density peaks. But the cut-off distance and clustering …
superiority of clustering by finding the density peaks. But the cut-off distance and clustering …
DFC: Density fragment clustering without peaks
The density peaks clustering (DPC) algorithm is a novel density-based clustering approach.
Outliers can be spotted and excluded automatically, and clusters can be found regardless of …
Outliers can be spotted and excluded automatically, and clusters can be found regardless of …
HaloDPC: An improved recognition method on halo node for density peak clustering algorithm
The density peaks clustering (DPC) is known as an excellent approach to detect some
complicated-shaped clusters with high-dimensionality. However, it is not able to detect …
complicated-shaped clusters with high-dimensionality. However, it is not able to detect …
ASCRClu: an adaptive subspace combination and reduction algorithm for clustering of high-dimensional data
The curse of dimensionality in high-dimensional data is one of the major challenges in data
clustering. Recently, a considerable amount of literature has been published on subspace …
clustering. Recently, a considerable amount of literature has been published on subspace …
A fuzzy density peaks clustering algorithm based on improved dna genetic algorithm and K-nearest neighbors
W Zhang, W Zang - Intelligence Science and Big Data Engineering: 8th …, 2018 - Springer
In recent times, a density peaks based clustering algorithm (DPC) that published in Science
was proposed in June 2014. By using a decision graph and finding out cluster centers from …
was proposed in June 2014. By using a decision graph and finding out cluster centers from …
Efficient Monte Carlo clustering in subspaces
CF Olson, DC Hunn, HJ Lyons - Knowledge and Information Systems, 2017 - Springer
Clustering of high-dimensional data is an important problem in many application areas,
including image classification, genetic analysis, and collaborative filtering. However, it is …
including image classification, genetic analysis, and collaborative filtering. However, it is …
[PDF][PDF] Fast and area-efficient hardware implementation of the k-means clustering algorithm
K-means clustering algorithm aims to partition data elements of an input dataset into K
clusters in which each data element belongs to the cluster with the nearest centroid. The …
clusters in which each data element belongs to the cluster with the nearest centroid. The …
Evolutionary subspace clustering using variable genome length
Subspace clustering is a data‐mining task that groups similar data objects and at the same
time searches the subspaces where similarities appear. For this reason, subspace clustering …
time searches the subspaces where similarities appear. For this reason, subspace clustering …