scholarly journals Improving Scalable K-Means++

Algorithms ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 6
Author(s):  
Joonas Hämäläinen ◽  
Tommi Kärkkäinen ◽  
Tuomo Rossi
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Random Projection ◽  
Divide And Conquer ◽  
High Dimensional ◽  
Data Generation ◽  
Large Scale Problems ◽  
Clustering Data ◽  
Very High ◽  
High Dimensional Clustering

Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for the initialization. The proposed methods are scalable and can be run in parallel, which make them suitable for initializing large-scale problems. In the experiments, comparison of the proposed methods to the K-means++ and K-means‖ methods is conducted using an extensive set of reference and synthetic large-scale datasets. Concerning the latter, a novel high-dimensional clustering data generation algorithm is given. The experiments show that the proposed methods compare favorably to the state-of-the-art by improving clustering accuracy and the speed of convergence. We also observe that the currently most popular K-means++ initialization behaves like the random one in the very high-dimensional cases.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Shell-crossing in a ΛCDM Universe

2020 ◽  
Vol 501 (1) ◽  
pp. L71-L75
Author(s):  
Cornelius Rampf ◽  
Oliver Hahn
Keyword(s):  
Dark Matter ◽  
Perturbation Theory ◽  
Large Scale ◽  
Cold Dark Matter ◽  
Three Dimensional ◽  
Random Initial Data ◽  
Recursion Relations ◽  
Indispensable Tool ◽  
First Time ◽  
Very High

ABSTRACT Perturbation theory is an indispensable tool for studying the cosmic large-scale structure, and establishing its limits is therefore of utmost importance. One crucial limitation of perturbation theory is shell-crossing, which is the instance when cold-dark-matter trajectories intersect for the first time. We investigate Lagrangian perturbation theory (LPT) at very high orders in the vicinity of the first shell-crossing for random initial data in a realistic three-dimensional Universe. For this, we have numerically implemented the all-order recursion relations for the matter trajectories, from which the convergence of the LPT series at shell-crossing is established. Convergence studies performed at large orders reveal the nature of the convergence-limiting singularities. These singularities are not the well-known density singularities at shell-crossing but occur at later times when LPT already ceased to provide physically meaningful results.

scholarly journals Assessing Annual Actual Evapotranspiration Based on Climate, Topography and Soil in Natural and Agricultural Ecosystems

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 20
Author(s):  
Kleoniki Demertzi ◽  
Vassilios Pisinaras ◽  
Emanuel Lekakis ◽  
Evangelos Tziritis ◽  
Konstantinos Babakos ◽  
...  
Keyword(s):  
Water Balance ◽  
Large Scale ◽  
Hydrological Model ◽  
Actual Evapotranspiration ◽  
Irrigated Agriculture ◽  
Climate Data ◽  
Natural Ecosystems ◽  
Loading Effects ◽  
Very High ◽  
Of Greece

Simple formulas for estimating annual actual evapotranspiration (AET) based on annual climate data are widely used in large scale applications. Such formulas do not have distinct compartments related to topography, soil and irrigation, and for this reason may be limited in basins with high slopes, where runoff is the dominant water balance component, and in basins where irrigated agriculture is dominant. Thus, a simplistic method for assessing AET in both natural ecosystems and agricultural systems considering the aforementioned elements is proposed in this study. The method solves AET through water balance based on a set of formulas that estimate runoff and percolation. These formulas are calibrated by the results of the deterministic hydrological model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) for a reference surface. The proposed methodology is applied to the country of Greece and compared with the widely used climate-based methods of Oldekop, Coutagne and Turk. The results show that the proposed methodology agrees very well with the method of Turk for the lowland regions but presents significant differences in places where runoff is expected to be very high (sloppy areas and areas of high rainfall, especially during December–February), suggesting that the proposed method performs better due to its runoff compartment. The method can also be applied in a single application considering irrigation only for the irrigated lands to more accurately estimate AET in basins with a high percentage of irrigated agriculture.

scholarly journals Development of Efficient and Selective Processes for the Synthesis of Commercially Important Chlorinated Phenols

Organics ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 142-160
Author(s):  
Keith Smith ◽  
Gamal A. El-Hiti
Keyword(s):  
Lewis Acid ◽  
Large Scale ◽  
Commercial Application ◽  
Chlorinated Phenols ◽  
Sulphur Compounds ◽  
Inorganic Solids ◽  
Solvent Free Conditions ◽  
Commercially Important ◽  
Ortho Cresol ◽  
Very High

para-Selective processes for the chlorination of phenols using sulphuryl chloride in the presence of various sulphur-containing catalysts have been successfully developed. Several chlorinated phenols, especially those derived by para-chlorination of phenol, ortho-cresol, meta-cresol, and meta-xylenol, are of significant commercial importance, but chlorination reactions of such phenols are not always as regioselective as would be desirable. We, therefore, undertook the challenge of developing suitable catalysts that might promote greater regioselectivity under conditions that might still be applicable for the commercial manufacture of products on a large scale. In this review, we chart our progress in this endeavour from early studies involving inorganic solids as potential catalysts, through the use of simple dialkyl sulphides, which were effective but unsuitable for commercial application, and through a variety of other types of sulphur compounds, to the eventual identification of particular poly(alkylene sulphide)s as very useful catalysts. When used in conjunction with a Lewis acid such as aluminium or ferric chloride as an activator, and with sulphuryl chloride as the reagent, quantitative yields of chlorophenols can be obtained with very high regioselectivity in the presence of tiny amounts of the polymeric sulphides, usually in solvent-free conditions (unless the phenol starting material is solid at temperatures even above about 50 °C). Notably, poly(alkylene sulphide)s containing longer spacer groups are particularly para-selective in the chlorination of m-cresol and m-xylenol, while, ones with shorter spacers are particularly para-selective in the chlorination of phenol, 2-chlorophenol, and o-cresol. Such chlorination processes result in some of the highest para/ortho ratios reported for the chlorination of phenols.

scholarly journals Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

2021 ◽  
Vol 11 (2) ◽  
pp. 472
Author(s):  
Hyeongmin Cho ◽  
Sangkyun Lee
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Large Scale ◽  
High Dimensional Data ◽  
Quality Measures ◽  
Training Data ◽  
Measure Data ◽  
High Dimensional ◽  
Small Scale ◽  
Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

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