Related papers: Meteor Shower Detection with Density-Based Cluster…

Selecting an appropriate clustering method as well as an optimal number of clusters in road accident data is at times confusing and difficult. This paper analyzes shortcomings of different existing techniques applied to cluster…

A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for the…

Every moment, countless meteoroids enter our atmosphere unseen. The detection and measurement of meteors offer the unique opportunity to gain insights into the composition of our solar systems' celestial bodies. Researchers, therefore,…

Density-based clustering is the task of discovering high-density regions of entities (clusters) that are separated from each other by contiguous regions of low-density. DBSCAN is, arguably, the most popular density-based clustering…

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…

Separating meteor showers from the sporadic meteor background is critical for the study of both showers and the sporadic complex. The linkage of meteors to meteor showers, to parent bodies, and to other meteors is done using measures of…

Estimating the meteoroid flux density at centimetre to metre sizes is notoriously difficult. Yet it is an important endeavour, as these sizes represent the transition between small meteoroids that pose a risk to spacecraft, and the…

Accurate estimation of meteoroid bulk density is crucial for assessing spacecraft impact hazards from sub-millimeter to millimeter-sized meteoroids. Previous studies often used manual tuning or optimization methods to fit ablation and…

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…

In meteor science, the identification of meteor showers is a crucial and complex problem. The most common method is to perform a systematic search of a database of observed orbits using an orbit dissimilarity criterion (D-criterion) and an…

We study the problem of optimal estimation of the density cluster tree under various assumptions on the underlying density. Building up from the seminal work of Chaudhuri et al. [2014], we formulate a new notion of clustering consistency…

We present an algorithm developed to measure the fluxes of major meteor showers as observed in NASA's All-Sky Fireball Network cameras. Measurements of fluxes from the All-Sky cameras not only improve the Meteoroid Environment Office's…

In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio,…

Recent advancements in neutron and X-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 10$^{8}$ -- 10$^{10}$ data points), so that conventional…

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods…

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…

A cluster analysis was applied to the combined meteoroid orbit database derived from low-light level video observations by the SonotaCo consortium in Japan (64,650 meteors observed between 2007 and 2009) and by the Cameras for All-sky…

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…

Economic policy and research rely on the correct evaluation of the billions of high-frequency data points that we collect every day. Consistent clustering algorithms, like DBSCAN, allow us to make sense of the data in a useful way. However,…

We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which…