Stereochemical and conformational classification of the hexopyranose sugars using numerical clustering methods

1993 ◽  
Vol 49 (6) ◽  
pp. 1021-1031 ◽  
Author(s):  
F. H. Allen ◽  
S. Fortier
Keyword(s):  
Clustering Methods ◽  
Numerical Clustering

scholarly journals Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1723
Author(s):  
Hyun Cheol Jeong ◽  
Jaesung Jung ◽  
Byung O Kang
Keyword(s):  
Energy Storage ◽  
South Korea ◽  
Storage System ◽  
Battery Life ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Operational Strategies ◽  
Square Wave ◽  
Shaped Pattern

This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into ‘M-shaped’ and ‘square wave-shaped’ load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the ‘M-shaped’ pattern fixes a 2-cycle operation per day due to battery life, while the ‘square wave-shaped’ pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits.

A new classification of solvents based on chemometric empirical scale of parameters

1990 ◽  
Vol 55 (3) ◽  
pp. 644-652 ◽  
Author(s):  
Oldřich Pytela
Keyword(s):  
Causal Relation ◽  
Carbon Disulphide ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Solvent Polarity ◽  
Clustering Methods ◽  
Hexamethylphosphoric Triamide ◽  
Centroid Method ◽  
The Individual

The paper presents a classification of 51 solvents based on clustering in three-dimensional space formed by the empirical scale of PAC, PBC, and PPC parameters designed for interpretation of solvent effect on a model with cross-terms. For the classification used are the clustering methods of the nearest neighbour, of the furthest neighbour, of average bond, and the centroid method. As a result, the solvents have been divided into 8 classes denoted as: I - nonpolar-inert solvents (aliphatic hydrocarbons), IIp - nonpolar-polarizable (aromatic hydrocarbons, tetrachloromethane, carbon disulphide), IIb - nonpolar-basic (ethers, triethylamine), IIIp - little polar-polarizable (aliphatic halogen derivatives, substituted benzenes with heteroatom-containing substituents), IIIb - little polar-basic (cyclic ethers, ketones, esters, pyridine), IVa - polar-aprotic (acetanhydride, dialkylamides, acetonitrile, nitromethane, dimethyl sulfoxide, sulfolane), IVp - polar-protic (alcohols, acetic acid), and V - exceptional solvents (water, formamide, glycol, hexamethylphosphoric triamide). The information content of the individual parameters used for the classification has been determined. The classification is based primarily on solvent polarity/acidity (PAC), less on polarity/basicity (PBC), and the least on polarity/polarizability (PPC). Causal relation between chemical structure of solvent and its effect on the process taking place therein has been established.

Application of optimization tasks in cluster analysis

10.12737/7483 ◽  
2014 ◽  
Vol 8 (7) ◽  
pp. 0-0
Author(s):  
Олег Сдвижков ◽  
Oleg Sdvizhkov
Keyword(s):  
Cluster Analysis ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Homogeneous Groups ◽  
Finite Set ◽  
Minimum Number

Cluster analysis [3] is a relatively new branch of mathematics that studies the methods partitioning a set of objects, given a finite set of attributes into homogeneous groups (clusters). Cluster analysis is widely used in psychology, sociology, economics (market segmentation), and many other areas in which there is a problem of classification of objects according to their characteristics. Clustering methods implemented in a package STATISTICA [1] and SPSS [2], they return the partitioning into clusters, clustering and dispersion statistics dendrogram of hierarchical clustering algorithms. MS Excel Macros for main clustering methods and application examples are given in the monograph [5]. One of the central problems of cluster analysis is to define some criteria for the number of clusters, we denote this number by K, into which separated are a given set of objects. There are several dozen approaches [4] to determine the number K. In particular, according to [6], the number of clusters K - minimum number which satisfies where - the minimum value of total dispersion for partitioning into K clusters, N - number of objects. Among the clusters automatically causes the consistent application of abnormal clusters [4]. In 2010, proposed and experimentally validated was a method for obtaining the number of K by applying the density function [4]. The article offers two simple approaches to determining K, where each cluster has at least two objects. In the first number K is determined by the shortest Hamiltonian cycles in the second - through the minimum spanning tree. The examples of clustering with detailed step by step solutions and graphic illustrations are suggested. Shown is the use of macro VBA Excel, which returns the minimum spanning tree to the problems of clustering. The article contains a macro code, with commentaries to the main unit.

scholarly journals Comparison of Clustering Methods in the Context of Chemical Structure Similarity Based Classification of VOCs

2016 ◽  
Vol 20 ◽  
pp. 40-44
Author(s):  
Azian Azamimi Abdullah ◽  
Md. Altaf-Ul-Amin ◽  
Naoaki Ono ◽  
Nurlisa Yusuf ◽  
Ammar Zakaria ◽  
...  
Keyword(s):  
Chemical Structure ◽  
Clustering Methods ◽  
Structure Similarity

scholarly journals Density-based classification with the DENCLUE algorithm

2021 ◽  
Vol 24 (1) ◽  
pp. 269
Author(s):  
Mouhcine El Hassani ◽  
Noureddine Falih ◽  
Belaid Bouikhalene
Keyword(s):  
Spatial Clustering ◽  
Data Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Density Based Clustering ◽  
Marketing Data ◽  
Numerous Data ◽  
Bank Marketing ◽  
Probabilistic Nature

<p><span>Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.</span></p>

scholarly journals Typology of welfare states: quality criteria for governance and ecology

2018 ◽  
Vol 16 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Victor Koziuk ◽  
Oleksandr Dluhopolskyi ◽  
Yurij Hayda ◽  
Oksana Shymanska
Keyword(s):  
Welfare State ◽  
Well Being ◽  
Welfare States ◽  
Clustering Methods ◽  
Governance System ◽  
Government Efficiency ◽  
The Government ◽  
The Welfare State

In the 21st century, in addition to the generally well-known indicators of material well-being, in the modern paradigm of the welfare state, the quality of the ecological environment is gaining an ever-increasing role. Besides that, the modern definition of welfare state takes into account not only environmental dimension, but also the quality of institutions through the governance system that affects the supply of environmental goods. The study provides the classification of countries according to indicators that can ensure the identification of welfare states and the assessment of the classification role of the criteria for environmental state.The strong direct correlation between environmental state and government efficiency has been established. The results of the classification of the studied countries obtained by k-means clustering methods indicate the possibility of using the Environmental Performance Index (EPI), Government Effectiveness Index (GEI) and government expenditures indicators as complementary attributes to the classical criteria for the welfare state.The level of country EPI can be regarded as an important complementary criterion for the welfare state. The country environmental state is much more determined by the government efficiency, the quality of state institutions and their activities, rather than by an extensive increase in the funding of such institutions and environmental measures.

scholarly journals DeLUCS: Deep Learning for Unsupervised Classification of DNA Sequences

2021 ◽  
Author(s):  
Pablo Millan Arias ◽  
Fatemeh Alipour ◽  
Kathleen Hill ◽  
Lila Kari
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
Sequence Homology ◽  
Bacterial Genome ◽  
Unsupervised Classification ◽  
Gaussian Mixture ◽  
Majority Voting ◽  
Sequence Length ◽  
Clustering Methods

We present a novel Deep Learning method for the Unsupervised Classification of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Chaos Game Representations (CGRs) of primary DNA sequences, and generates “mimic” sequence CGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster label for each sequence. DeLUCS is able to cluster large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into families; three viral genome and gene datasets, averaging 1,300 sequences each, into virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means and Gaussian Mixture Models) for unlabelled data, by as much as 48%. DeLUCS is highly effective, it is able to classify datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence classification for previously unclassifiable datasets.

Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images

2009 ◽  
pp. 351-372
Author(s):  
C. Papaodysseus ◽  
P. Rousopoulos ◽  
D. Arabadjis ◽  
M. Panagopoulos ◽  
P. Loumou
Keyword(s):  
Elastic Properties ◽  
State Of The Art ◽  
The State ◽  
Automatic Identification ◽  
Clustering Methods ◽  
Microscopic Images ◽  
High Deformation ◽  
Third Stage ◽  
Exact Evaluation

In this chapter the state of the art is presented in the domain of automatic identification and classification of bodies on the basis of their deformed images obtained via microscope. The approach is illustrated by means of the case of automatic recognition of third-stage larvae from microscopic images of them in high deformation instances. The introduced methodology incorporates elements of elasticity theory, image processing, curve fitting and clustering methods; a concise presentation of the state of the art in these fields is given. Combining proper elements of these disciplines, we first evaluate the undeformed shape of a parasite given a digital image of a random parasite deformation instance. It is demonstrated that different orientations and deformations of the same parasite give rise to practically the same undeformed shape when the methodology is applied to the corresponding images, thus confirming the consistency of the approach. Next, a pattern recognition method is introduced to classify the unwrapped parasites into four families, with a high success rate. In addition, the methodology presented here is a powerful tool for the exact evaluation of the mechano-elastic properties of bodies from images of their deformation instances.

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