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Found 10 papers, 0 papers with code
Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method
A large bandwidth could lead to the over-smoothed density estimation in which the number of density peaks could be less than the true clusters, while a small bandwidth could lead to the under-smoothed density estimation in which spurious density peaks, or called the "ripple noise", would be generated in the estimated density.
Clustering by Hierarchical Nearest Neighbor Descent (H-NND)
Due to some beautiful and effective features, this IT structure proves well suited for data clustering.
IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family
But if we can effectively map those IT structures into a visualized space in which the salient features of those undesired edges are preserved, then the undesired edges in the IT structures can still be visually determined in a visualization environment.
A general framework for the IT-based clustering methods
Previously, we proposed a physically inspired rule to organize the data points in a sparse yet effective structure, called the in-tree (IT) graph, which is able to capture a wide class of underlying cluster structures in the datasets, especially for the density-based datasets.
Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
In our physically inspired in-tree (IT) based clustering algorithm and the series after it, there is only one free parameter involved in computing the potential value of each point.
Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
In our previous works, we proposed a physically-inspired rule to organize the data points into an in-tree (IT) structure, in which some undesired edges are allowed to occur.
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering
Scientists in many fields have the common and basic need of dimensionality reduction: visualizing the underlying structure of the massive multivariate data in a low-dimensional space.
Clustering based on the In-tree Graph Structure and Affinity Propagation
A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree.
An Effective Semi-supervised Divisive Clustering Algorithm
Nowadays, data are generated massively and rapidly from scientific fields as bioinformatics, neuroscience and astronomy to business and engineering fields.
Nearest Descent, In-Tree, and Clustering
In particular, the rule of ND works to select the nearest node in the descending direction of potential as the parent node of each node, which is in essence different from the classical Gradient Descent or Steepest Descent.
