Results 31 to 40 of about 1,206,915 (318)
Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
Algorithms, 2017 We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online.
Xin Tian, Song Li
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Hybridization between deep learning algorithms and neutrosophic theory in medical image processing: A survey [PDF]
Neutrosophic Sets and Systems, 2021 Deep learning can successfully extract data features based on dealing greatly with nonlinear problems. Deep learning has the highest performance in medical image analysis and diagnosis.
N.N. Mostafa, K. Ahmed, I. El-Henawy
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A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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ACM Transactions on Knowledge Discovery from Data, 2005 We consider the following problem: given a set of clusterings, find a single clustering that agrees as much as possible with the input clusterings. This problem, clustering aggregation , appears naturally in various contexts.
Aristides Gionis +2 more
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Drinking water accessibility typologies in low- and middle-income countries
Environmental Research Letters, 2023 We present a data-driven typology framework for understanding patterns in drinking water accessibility across low- and middle-income countries. Further, we obtain novel typology-specific insights regarding the relationships between possible explanatory ...
Hichul Chung, Emily Kumpel, Jimi Oke
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Analysis of key university leadership factors based on their international rankings (QS World University Rankings and Times Higher Education) [PDF]
Problems and Perspectives in Management, 2020 In the context of globalization of the educational services market, competition between universities is becoming more intense. This manifests itself, among other things, in the struggle for positions in international university rankings.
Maxim Polyakov +3 more
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Clustering technique for conceptual clusters [PDF]
Proceedings of the 11th edition of the International Workshop on Smalltalk Technologies, 2016 Clustering aims to classify elements into groups called classes or clusters. Clustering is used in reverse-engineering to help to understand legacy software. It is also a tech-nic used in re-engineering to propose gatherings of software entities to engineers who can then accept them or not. This paper presents a Pharo implementation of an iterative and
Govin, Brice +3 more
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Sequence spaces M ( ϕ ) $M(\phi)$ and N ( ϕ ) $N(\phi)$ with application in clustering
Journal of Inequalities and Applications, 2017 Distance measures play a central role in evolving the clustering technique. Due to the rich mathematical background and natural implementation of l p $l_{p}$ distance measures, researchers were motivated to use them in almost every clustering process ...
Mohd Shoaib Khan +3 more
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Simulating burn severity maps at 30 meters in two forested regions in California
Environmental Research Letters, 2022 Climate change is altering wildfire and vegetation regimes in California’s forested ecosystems. Present day fires are seeing an increase in high burn severity area and high severity patch size.
Jonathan A Sam +7 more
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, 2008 Clustering techniques are used to arrange genes in some natural way, that is, to organize genes into groups or clusters with similar behavior across relevant tissue samples (or cell lines). These techniques can also be applied to tissues rather than genes.
McLachlan, G. J., Bean, R. W., Ng, S. K.
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