scholarly journals Interactive exploration and modeling of large data sets: a case study with Venus light scattering data

1996 ◽  
Author(s):  
J.J. van Wijk ◽  
H.J.W. Spoelder ◽  
W.-J. Knibbe ◽  
K.E. Shahroudi
Keyword(s):  
Light Scattering ◽  
Large Data ◽  
Large Data Sets ◽  
Scattering Data ◽  
Data Sets ◽  
Interactive Exploration

scholarly journals Ultrafast calculation of diffuse scattering from atomistic models

2019 ◽  
Vol 75 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Joseph A. M. Paddison
Keyword(s):  
Diffuse Scattering ◽  
Large Data ◽  
Large Data Sets ◽  
Scattering Data ◽  
Frequency Noise ◽  
Data Sets ◽  
Fast Method ◽  
Crystalline Materials ◽  
Atomistic Models ◽  
Dynamics Simulations

Diffuse scattering is a rich source of information about disorder in crystalline materials, which can be modelled using atomistic techniques such as Monte Carlo and molecular dynamics simulations. Modern X-ray and neutron scattering instruments can rapidly measure large volumes of diffuse-scattering data. Unfortunately, current algorithms for atomistic diffuse-scattering calculations are too slow to model large data sets completely, because the fast Fourier transform (FFT) algorithm has long been considered unsuitable for such calculations [Butler & Welberry (1992). J. Appl. Cryst. 25, 391–399]. Here, a new approach is presented for ultrafast calculation of atomistic diffuse-scattering patterns. It is shown that the FFT can actually be used to perform such calculations rapidly, and that a fast method based on sampling theory can be used to reduce high-frequency noise in the calculations. These algorithms are benchmarked using realistic examples of compositional, magnetic and displacive disorder. They accelerate the calculations by a factor of at least 102, making refinement of atomistic models to large diffuse-scattering volumes practical.

scholarly journals Using Large Data Sets to Forecast House Prices: A Case Study of Twenty U.S. States

2011 ◽  
Vol 20 (2) ◽  
pp. 161-190 ◽  
Author(s):  
Rangan Gupta ◽  
Alain Kabundi ◽  
Stephen Miller
Keyword(s):  
House Prices ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets

scholarly journals Comparative Performance of Supertree Algorithms in Large Data Sets Using the Soapberry Family (Sapindaceae) as a Case Study

2010 ◽  
Vol 60 (1) ◽  
pp. 32-44 ◽  
Author(s):  
Sven Buerki ◽  
Félix Forest ◽  
Nicolas Salamin ◽  
Nadir Alvarez
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Comparative Performance

scholarly journals Penerapan Metode Clustering Dengan Algoritma K-Means Tindak Kejahatan Pencurian di Kabupaten Asahan

2021 ◽  
Vol 1 (1) ◽  
pp. 7-14
Author(s):  
Nur Afni Syahpitri Damanik ◽  
Irianto Irianto ◽  
Dahriansah Dahriansah
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Historical Data ◽  
Large Data ◽  
Large Data Sets ◽  
Annual Data ◽  
Data Sets ◽  
Process Data ◽  
Different Characteristics

Abstract:Theft is the illegal taking of property or belongings of another person without the permission of the owner. The most common crime problem in Asahan District is theft, so that the POLRES is still having trouble determining which areas are often the crime of theft. With this problem, we need to do a grouping for areas where theft often occurs, so the process used  is the data mining process. Data mining is one of the processes of Knowledge Discovery from Databases (KDD). KDD is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data sets. One of the techniques known in data mining is clustering technique. The K-Means method is a method for clustering techniques, K- Means is a method that partitions data into groups so that data with the same characteristics are entered into the same set of groups and data with different characteristics are grouped into other groups. The attributes used in grouping this data are annual data, namely 2015, 2016, 2017, 2018, 2019. A case study of 9 POLSEK in the Asahan. Keywords: Data Mining, Clustering, K-Means Algorithm, Theft Crimes Grouping.  Abstrak: Pencurian merupakan pengambilan properti atau barang milik orang lain secara tidak sah tanpa ijin dari pemilik. Masalah tindak kejahatan yang paling banyak terjadi di Kabupaten Asahan adalah tindak kejahatan pencurian sehingga pihak POLRES masih kesulitan untuk menentukan daerah mana saja yang sering terjadi tindak kejahatan pencuriaan. Dengan adanya masalah ini kita perlu melakukan pengelompokan untuk daerah mana saja yang sering terjadi tindak pencurian maka proses yang digunakan adalah proses data mining. Data mining adalah salah satu proses dari Knowledge Discovery from Databases (KDD). KDD adalah kegiatan yang meliputi pengumpulan, pemakaian data, historis untuk menemukan keteraturan, pola atau hubungan dalam set data besar. Salah satu teknik yang di kenal dalam data mining adalah teknik clustering. Metode K-Means merupakan metode untuk teknik clustering, K-Means adalah metode yang mempartisi data kedalam kelompok sehingga data berkarakteristik sama dimasukan kedalam set kelompok yang sama dan data yang berkerakteristik berbeda dikelompokkan ke dalam kelompok yang lain. Atribut yang di gunakan dalam pengelomokan data ini adalah data pertahun yaitu tahun 2015, 2016, 2017, 2018, 2019. Studi kasus pada 9 POLSEK yang ada di daerah kabupaten Asahan. Kata kunci: Data Mining, Clustering, Algoritma K-Means, Pengelompokan Tindak Kejahatan  Pencurian.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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