Teng Qiu

Learn More
Clustering analysis aims to discover the underlying clusters in the data points according to their similarities. It has wide applications ranging from bioinformatics to astronomy. Here, we proposed a Generalized Affinity Propagation (GAP) clustering algorithm. Data points are first organized in a sparsely connected in-tree (IT) structure by a physically(More)
Clustering analysis is a method to organize raw data into categories based on a measure of similarity. It has been successfully applied to diverse fields from science to business and engineering. By endowing data points with physical meaning like particles in the physical world and then leaning their evolving tendency of moving from higher to lower(More)
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. However, many dimensionality reduction methods confront the so-called " crowding problem " that clusters tend to overlap with each other in the embedding. Previously,(More)
The accumulation of defects, and in particular He bubbles, can have significant implications for the performance of materials exposed to the plasma in magnetic-confinement nuclear fusion reactors. Some of the most promising candidates for deployment into such environments are nanocrystalline materials as the engineering of grain boundary density offers the(More)
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 removing those undesired or redundant edges, this IT structure is divided into several separate parts, each representing one cluster. In this work, we seek to prevent the undesired(More)
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 well revealed. Although there are some edges in the IT structure requiring to be removed, such undesired edges are generally distinguishable from other edges and thus are easy to(More)
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. In this work, based on the Delaunay Triangulation or its dual Voronoi tessellation, we propose a nonparametric process to compute potential values by the local information. This(More)