7 citations · 38 across the 10 of their papers we have counts for
10 papers
Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method
Teng Qiu, Yongjie Li
Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the…
Clustering by Hierarchical Nearest Neighbor Descent (H-NND)
Teng Qiu, Yongjie Li
Previously in 2014, we proposed the Nearest Descent (ND) method, capable of generating an efficient Graph, called the in-tree (IT). Due to some beautiful and effective features, th…
IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family
Teng Qiu, Yongjie Li
Previously, we proposed a physically-inspired method to construct data points into an effective in-tree (IT) structure, in which the underlying cluster structure in the dataset is…
A general framework for the IT-based clustering methods
Teng Qiu, Yongjie Li
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 c…
Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
Teng Qiu, Yongjie Li
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
Teng Qiu, Yongjie Li
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. By rem…
An Effective Semi-supervised Divisive Clustering Algorithm
Teng Qiu, Yongjie Li
Nowadays, data are generated massively and rapidly from scientific fields as bioinformatics, neuroscience and astronomy to business and engineering fields. Cluster analysis, as one…
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering
Teng Qiu, Yongjie Li
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 spa…
Clustering based on the In-tree Graph Structure and Affinity Propagation
Teng Qiu, Yongjie Li
A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree. This ND-based Intree str…
Nearest Descent, In-Tree, and Clustering
Teng Qiu, Kaifu Yang, Chaoyi Li +1
In this paper, we propose a physically inspired graph-theoretical clustering method, which first makes the data points organized into an attractive graph, called In-Tree, via a phy…