Colloquium: Machine learning in nuclear physics
A Boehnlein, M Diefenthaler, N Sato, M Schram… - Reviews of modern …, 2022 - APS
Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …
scientific research. These techniques are being applied across the diversity of nuclear …
From clustering to clustering ensemble selection: A review
Clustering, as an unsupervised learning, is aimed at discovering the natural groupings of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
DBSCAN revisited, revisited: why and how you should (still) use DBSCAN
At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-
Fixability, and Approximation” that won the conference's best paper award. In this technical …
Fixability, and Approximation” that won the conference's best paper award. In this technical …
A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems
Width deviation is an important metric for evaluating the quality of a hot-rolled strip in steel
production systems. This paper considers a width deviation prediction problem and …
production systems. This paper considers a width deviation prediction problem and …
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
The evaluation of unsupervised outlier detection algorithms is a constant challenge in data
mining research. Little is known regarding the strengths and weaknesses of different …
mining research. Little is known regarding the strengths and weaknesses of different …
Identification of cell types from single-cell transcriptomes using a novel clustering method
Motivation: The recent advance of single-cell technologies has brought new insights into
complex biological phenomena. In particular, genome-wide single-cell measurements such …
complex biological phenomena. In particular, genome-wide single-cell measurements such …
A survey on unsupervised outlier detection in high‐dimensional numerical data
High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …
Density‐based clustering
Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …
clustering, a cluster is a set of data objects spread in the data space over a contiguous …
Unsupervised clickstream clustering for user behavior analysis
Online services are increasingly dependent on user participation. Whether it's online social
networks or crowdsourcing services, understanding user behavior is important yet …
networks or crowdsourcing services, understanding user behavior is important yet …
Density‐based clustering
Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …
clustering, a cluster is a set of data objects spread in the data space over a contiguous …