Related papers: A density-based clustering algorithm for the CYGNO…
A large number of high-energy and heavy-ion experiments successfully used Time Projection Chamber (TPC) as central tracker and particle identification detector. However, the performance requirements on TPC for new high-rate particle…
Time Projection Chambers equipped with Gas Electron Multipliers and optical readout by scientific CMOS cameras are a promising technology for low-energy particle detection, as demonstrated by the CYGNO experiment. To help identify the…
In recent years Gas Electron Multipliers have proven to be reliable amplification stages at high beam rates, and can be used also in Time Projection Chambers. Our group developed a 1 dm$^3$ active volume double-GEM TPC, with spatial…
The detection of photons produced during the avalanche development in gas chambers has been the subject of detailed studies in the past. The great progresses achieved in last years in the performance of micro-pattern gas detectors on one…
Finding a suitable density function is essential for density-based clustering algorithms such as DBSCAN and DPC. A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in these…
DBSCAN has been widely used in density-based clustering algorithms. However, with the increasing demand for Multi-density clustering, previous traditional DSBCAN can not have good clustering results on Multi-density datasets. In order to…
Optical readout of large Time Projection Chambers (TPCs) with multiple Gas Electron Multipliers (GEMs) amplification stages has shown to provide very interesting performances for high energy particle tracking. Proposed applications for…
Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk…
Deep optical images are often crowded with overlapping objects. This is especially true in the cores of galaxy clusters, where images of dozens of galaxies may lie atop one another. Accurate measurements of cluster properties require…
This paper describes the incremental behaviours of Density based clustering. It specially focuses on the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and its incremental approach.DBSCAN relies on a density…
We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement over the popular DBSCAN algorithm. The accelerated HDBSCAN*…
Clustering analysis, a classical issue in data mining, is widely used in various research areas. This article aims at proposing a self-adaption grey DBSCAN clustering (SAG-DBSCAN) algorithm. First, the grey relational matrix is used to…
Optical readout of GEM based devices by means of high granularity and low noise CMOS sensors allows to obtain very interesting tracking performance. Space resolution of the order of tens of $\mu$m were measured on the GEM plane along with…
DBSCAN is a popular density-based clustering algorithm. It computes the $\epsilon$-neighborhood graph of a dataset and uses the connected components of the high-degree nodes to decide the clusters. However, the full neighborhood graph may…
Clustering is a cornerstone of modern data analysis. Detecting clusters in exploratory data analyses (EDA) requires algorithms that make few assumptions about the data. Density-based clustering algorithms are particularly well-suited for…
Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based…
DBSCAN is a classical density-based clustering procedure with tremendous practical relevance. However, DBSCAN implicitly needs to compute the empirical density for each sample point, leading to a quadratic worst-case time complexity, which…
The traditional algorithms do not meet the latest multiple requirements simultaneously for objects. Density-based method is one of the methodologies, which can detect arbitrary shaped clusters where clusters are defined as dense regions…
HDBSCAN is a density-based clustering algorithm that constructs a cluster hierarchy tree and then uses a specific stability measure to extract flat clusters from the tree. We show how the application of an additional threshold value can…
We develop a novel algorithm for characterizing Deep Sub-Electron Read Noise (DSERN) image sensors. This algorithm is able to simultaneously compute maximum likelihood estimates of quanta exposure, conversion gain, bias, and read noise of…