Assessing and Grouping Chemicals Applying Partial Ordering Alkyl Anilines as an Illustrative Example

2018 ◽  
Vol 21 (5) ◽  
pp. 349-357
Author(s):  
Lars Carlsen ◽  
Rainer Bruggemann
Keyword(s):  
Cluster Analysis ◽  
Environmental Chemistry ◽  
Indicator System ◽  
Partial Ordering ◽  
Hasse Diagram ◽  
Cluster Method ◽  
Wide Field ◽  
Substituted Anilines ◽  
Free Graph ◽  
Component Structure

Aim and Objective: In chemistry, there is a long tradition in classification. Usually, methods are adopted from the wide field of cluster analysis. The present study focusses on the application of partial ordering methodology for the classification of 21 alkyl substituted anilines. Materials and Methods: The analyses are based on the concepts from partial order methodology and cluster analyses. Here, with the example of 21 alkyl anilines, we show that concepts taken out from the mathematical discipline of partially ordered sets may be applied for classification. The chemical compounds are described by a multi-indicator system. For the present study four indicators, mainly taken from the field of environmental chemistry were applied and a graph of the ordering (Hasse diagram) was constructed. Results: A Hasse diagram is an acyclic, transitively reduced, triangle-free graph that may have several graph-theoretical components. The Hasse diagram has been directed from a structural chemical point of view. Two cluster analysis methods are applied (K-means and a hierarchical cluster method) and compared with the results from the Hasse diagram. In both cases, the partitioning of the set of 21 compounds by the component structure of the Hasse diagram appears to be better interpretable. Conclusion: It is shown that the partial ordering approach indeed can be used for classification in the present case. However, it must be clearly stated that a guarantee for meaningful results, in general, cannot be given. For that, further theoretical work is needed.

scholarly journals Inequalities in the European Union—A Partial Order Analysis of the Main Indicators

2021 ◽  
Vol 13 (11) ◽  
pp. 6278
Author(s):  
Lars Carlsen ◽  
Rainer Bruggemann
Keyword(s):  
Indicator System ◽  
Partial Ordering ◽  
Disposable Income ◽  
The European Union ◽  
Member States ◽  
Eu Member States ◽  
Per Capita ◽  
Educational Aspect ◽  
The Eu

The inequality within the 27 European member states has been studied. Six indicators proclaimed by Eurostat to be the main indicators charactere the countries: (i) the relative median at-risk-of-poverty gap, (ii) the income distribution, (iii) the income share of the bottom 40% of the population, (iv) the purchasing power adjusted GDP per capita, (v) the adjusted gross disposable income of households per capita and (vi) the asylum applications by state of procedure. The resulting multi-indicator system was analyzed applying partial ordering methodology, i.e., including all indicators simultaneously without any pretreatment. The degree of inequality was studied for the years 2010, 2015 and 2019. The EU member states were partially ordered and ranked. For all three years Luxembourg, The Netherlands, Austria, and Finland are found to be highly ranked, i.e., having rather low inequality. Bulgaria and Romania are, on the other hand, for all three years ranked low, with the highest degree of inequality. Excluding the asylum indicator, the risk-poverty-gap and the adjusted gross disposable income were found as the most important indicators. If, however, the asylum application is included, this indicator turns out as the most important for the mutual ranking of the countries. A set of additional indicators was studied disclosing the educational aspect as of major importance to achieve equality. Special partial ordering tools were applied to study the role of the single indicators, e.g., in relation to elucidate the incomparability of some countries to all other countries within the union.

Grey Incidence Cluster Analysis of Colliery Security Management Evaluating Indicator

2013 ◽  
Vol 753-755 ◽  
pp. 2963-2966 ◽  
Author(s):  
Shu Ying Zhao ◽  
Chen Sheng Yang ◽  
Sheng Wei Song
Keyword(s):  
System Theory ◽  
Cluster Analysis ◽  
Safety Management ◽  
Coal Mines ◽  
Security Management ◽  
Grey System Theory ◽  
Cluster Method ◽  
Grey System ◽  
Management Evaluation ◽  
Cluster Evaluation

It is necessary to exactly classify the evaluating indicator of colliery security management, and the classification is also the foundation and prerequisite of colliery security management and evaluation. In this paper, reasonable classification to colliery security management and evaluation is carried by the method of grey incidence cluster. Grey incidence cluster is one kind of grey cluster evaluation in grey system theory. On the basis of lots of investigation, analysis and summary, sample nine coal mines, 13 safety management evaluation，calculate their gray absolute degree, taken given threshold r=0.75，classify these indicators appropriately to simplify the evaluation and examination indicator. Evaluating indicator cluster method is simple and convenient and has the character of strong applicability.

scholarly journals Two different approaches to the affective profiles model: median splits (variable-oriented) and cluster analysis (person-oriented)

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1380 ◽  
Author(s):  
Danilo Garcia ◽  
Shane MacDonald ◽  
Trevor Archer
Keyword(s):  
Cluster Analysis ◽  
Negative Affect ◽  
Weighted Average ◽  
Well Being ◽  
Two Dimensions ◽  
Self Report ◽  
Cluster Method ◽  
And Gender ◽  
And Cluster Analysis ◽  
Oriented Approach

Background.The notion of the affective system as being composed of two dimensions led Archer and colleagues to the development of the affective profiles model. The model consists of four different profiles based on combinations of individuals’ experience of high/low positive and negative affect: self-fulfilling, low affective, high affective, and self-destructive. During the past 10 years, an increasing number of studies have used this person-centered model as the backdrop for the investigation of between and within individual differences in ill-being and well-being. The most common approach to this profiling is by dividing individuals’ scores of self-reported affect using the median of the population as reference for high/low splits. However, scores just-above and just-below the median might become high and low by arbitrariness, not by reality. Thus, it is plausible to criticize the validity of this variable-oriented approach. Our aim was to compare the median splits approach with a person-oriented approach, namely, cluster analysis.Method.The participants (N= 2, 225) were recruited through Amazons’ Mechanical Turk and asked to self-report affect using the Positive Affect Negative Affect Schedule. We compared the profiles’homogeneityandSilhouette coefficientsto discern differences in homogeneity and heterogeneity between approaches. We also conducted exact cell-wise analyses matching the profiles from both approaches and matching profiles and gender to investigate profiling agreement with respect to affectivity levels and affectivity and gender. All analyses were conducted using the ROPstat software.Results.The cluster approach (weighted average of clusterhomogeneity coefficients= 0.62,Silhouette coefficients= 0.68) generated profiles with greater homogeneity and more distinctive from each other compared to the median splits approach (weighted average of clusterhomogeneity coefficients= 0.75,Silhouette coefficients= 0.59). Most of the participants (n= 1,736, 78.0%) were allocated to the same profile (Rand Index= .83), however, 489 (21.98%) were allocated to different profiles depending on the approach. Both approaches allocated females and males similarly in three of the four profiles. Only the cluster analysis approach classified men significantly more often than chance to a self-fulfilling profile (type) and females less often than chance to this very same profile (antitype).Conclusions.Although the question whether one approach is more appropriate than the other is still without answer, the cluster method allocated individuals to profiles that are more in accordance with the conceptual basis of the model and also to expected gender differences. More importantly, regardless of the approach, our findings suggest that the model mirrors a complex and dynamic adaptive system.

scholarly journals Cluster analysis of time series K-Means line loss based on DWT distance

2021 ◽  
Vol 2137 (1) ◽  
pp. 012063
Author(s):  
Liming Song ◽  
Zhimin Chen ◽  
XinXin Meng ◽  
Shuai Kang
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Indicator System ◽  
Analysis Model ◽  
Running Time ◽  
Loss Analysis ◽  
Model Based ◽  
Traditional Algorithm ◽  
Line Loss ◽  
Analysis Of Time Series

Abstract This paper constructs an indicator system composed of inherent attributes and time characteristics of the line based on the line loss, and proposes a K-Means line loss cluster analysis model based on this indicator system. The line is classified according to the clustering results. The result is 314.51 on the CH index (Calinski Harabasz Index), 0.19 on the Silhouette Cofficient (Silhouette Cofficient), and a running time of 0.508s. Compared with the traditional algorithm, it is greatly improved. The field of line loss analysis has guiding significance.

scholarly journals Detecting Fraudulent Interviewers by Improved Clustering Methods – The Case of Falsifications of Answers to Parts of a Questionnaire

2016 ◽  
Vol 32 (3) ◽  
pp. 643-660 ◽  
Author(s):  
Samuel De Haas ◽  
Peter Winker
Keyword(s):  
Cluster Analysis ◽  
Data Quality ◽  
Survey Data ◽  
Empirical Research ◽  
A Priori ◽  
Cluster Method ◽  
Clustering Methods ◽  
New Approach ◽  
Bootstrap Approach ◽  
Synthetic Datasets

Abstract Falsified interviews represent a serious threat to empirical research based on survey data. The identification of such cases is important to ensure data quality. Applying cluster analysis to a set of indicators helps to identify suspicious interviewers when a substantial share of all of their interviews are complete falsifications, as shown by previous research. This analysis is extended to the case when only a share of questions within all interviews provided by an interviewer is fabricated. The assessment is based on synthetic datasets with a priori set properties. These are constructed from a unique experimental dataset containing both real and fabricated data for each respondent. Such a bootstrap approach makes it possible to evaluate the robustness of the method when the share of fabricated answers per interview decreases. The results indicate a substantial loss of discriminatory power in the standard cluster analysis if the share of fabricated answers within an interview becomes small. Using a novel cluster method which allows imposing constraints on cluster sizes, performance can be improved, in particular when only few falsifiers are present. This new approach will help to increase the robustness of survey data by detecting potential falsifiers more reliably.

An Empirical Study to Understand the Consumer Buying Behaviour in Ethnicwear Market in India through the Application of Factor and Cluster Analysis

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Sougata Banerjee ◽  
Sankar Sarkar
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Reference Group ◽  
Factor Loading ◽  
Cluster Method ◽  
Store Attributes ◽  
Group 5 ◽  
And Cluster Analysis ◽  
Buying Behaviour

In the study the researchers tried to cover up the present scenario of ethnic wear market in India and the consumer behaviorism. Thirty-one variables were selected from the study of (Gurunathan & Krishnakumar, 2013). 10 variables were taken on consumer characteristics, 3 variables on promotional techniques of the brand, 5 on influence of reference group, 5 on product attributes and 8 on store attributes were chosen from the study and Five point Likert Scale for opinion and responses. Exploratory factor analysis was run to understand the consumer buying styles in ethnic wear market and to identify the important indicators behind the purchase decision. Ten components or ten distinct types of customers were extracted through Varimax method and rotated component matrix namely Rational Purchasers, Influenced Shoppers, Quality Gift Purchasers, Promotion Driven Customers, Unplanned Purchasers, Passionate Consumers, Planned Purchasers, Customers looking for Card Facilities, Customers having Brand Knowledge and Brand Aware Customers. Four indicators on the basis of highest factor loading extracted from exploratory factor analysis were chosen for cluster analysis namely parking facilities, brand consciousness, preferences of parents and advertisement. Cluster analysis was done first by hierarchical method to deduce number of clusters which can be formed, and then the data was further processed through Ward Method in K-Means Cluster Method. Four distinct and differentiating segment namely emotional, rational, value driven and traditional modern were concluded with discrete characteristics.

scholarly journals Elementary steps in heterogeneous catalysis: The basis for environmental chemistry

2017 ◽  
Vol 22 (1-2) ◽  
pp. 11-41 ◽  
Author(s):  
Gerhard Ertl ◽  
Maria Zielińska ◽  
Małgorzata Rajfur ◽  
Maria Wacławek
Keyword(s):  
Heterogeneous Catalysis ◽  
Chemical Reaction ◽  
Environmental Chemistry ◽  
Large Fraction ◽  
Atomic Scale ◽  
Wide Field ◽  
Industrial Chemistry ◽  
Elementary Steps ◽  
Catalytic Processes ◽  
Petroleum Refineries

Abstract Catalysis is an alternative way for reaching an immediate formation of a product, because of a lower energy barrier (between the molecules and the catalysts). Heterogeneous catalysis comprises the acceleration of a chemical reaction through interaction of the molecules involved with the surface of a solid. It is a discipline, which involves all the different aspects of chemistry: inorganic and analytical chemistry in order to characterize the catalysts and the forms of these catalysts. The industrial chemistry puts all these things together to understand the solid chemical handling, chemical reaction and energy engineering and the heat and mass transfer in these catalytic processes. Very often there are more than one, but several products, then the role of the catalyst is not so much related to activity, but to selectivity. The underlying elementary steps can now be investigated down to the atomic scale as will be illustrated mainly with two examples: the oxidation of carbon monoxide (car exhaust catalyst) and the synthesis of ammonia (the basis for nitrogen fertilizer). There is a huge market for the catalysts themselves despite of their high costs. A large fraction is used for petroleum refineries, automotive and industrial cleaning processes. The catalytic processes is a wide field and there are still many problems concerning energy conservation and energy transformation, so there is much to do in the future.

scholarly journals Implementation of the Two Step Cluster Method on the National Labor Force Survey Data (Sakernas)

2018 ◽  
Vol 2 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Maya Deanti ◽  
Farit Mochamad Afendi ◽  
Aam Alamudi
Keyword(s):  
Cluster Analysis ◽  
Labor Force ◽  
Survey Data ◽  
Occupational Safety ◽  
Mixed Data ◽  
Cluster Method ◽  
Foreign Workers ◽  
Data Types ◽  
Labor Force Survey ◽  
Force Data

MAYA DEANTI. Implementation of Two Step Cluster Method on National Labor Force Survey Data (Sakernas) 2017 Bogor Regency. Supervised by FARIT MOCHAMAD AFENDI and AAM ALAMUDI.        Five labor issues in Indonesia that have not been resolved by 2017 are termination of employment due to digitalization or automation, labor informalization, BPJS, high accident and occupational safety (K3), and outsourcing. In addition, the increasing number of Foreign Workers (TKA) in Indonesia can affect the decrease in local employment opportunities. Therefore, in this study will be carried out clustering to the labor force data to determine the condition of employment in Indonesia, especially Bogor regency. However, this labor force data has considerable observation with mixed data types, namely numerical and categorical. Regular cluster analysis can not be applied directly to the condition of the data, so that to be used in this research is a Two Step Cluster analysis which is a modification of existing cluster analysis. This Two Step Cluster analysis produces 3 clusters, with the characteristics of each cluster that is cluster 1 consisting of resident households or unemployed, cluster 2 consists of self-employed residents, and cluster 3 with the majority of the population working as laborers or employees. This clustering is based on work aspect only because the demography and education aspect of Bogor Regency is quite uniform.   Keywords: cluster analysis, cluster, Two Step Cluster, uniform

scholarly journals SME COMPETITIVENESS CLUSTER ANALYSIS IN EAST JAVA

2019 ◽  
Vol 4 (2) ◽  
pp. 86
Author(s):  
Muslih Muslih ◽  
Angga Erlando
Keyword(s):  
Cluster Analysis ◽  
Cluster Method ◽  
Economic Openness ◽  
The Face ◽  
Increase Efficiency

This study aims to analyse the competitiveness of the small and medium industries in East Java in the face of global economic openness. The method used is a cluster to find out the factors that influence the competitiveness of Small and Medium Industries (SME) by grouping them into groups based on similarity of characters. The use of the cluster method is carried out hierarchically, or processed through a series of successively fusing objects into groups. Based on the results of identification and analysis, then the conclusion, there are three cluster findings based on competitiveness categories. Cluster I is SME with low competitiveness, Cluster II is SME with high competitiveness, and Cluster III is SME with medium competitiveness. SME that have high competitiveness are SME that can increase efficiency in 2 fields, namely Production and Marketing. While SME that have medium competitiveness are SME that are superior in technology, so they can be classified in the creative industry.Keywords: Competitiveness, Cluster, SME, East JawaJEL Classification: O0; O1; O2; L6

scholarly journals Information Technology Education Policy Framework for Developing Countries: Survey and Cluster Analysis of Worldwide Patterns in Information Technology Education

10.28945/3311 ◽  
2009 ◽  
Author(s):  
Martiniano Jake III Parawan Neri
Keyword(s):  
Information Technology ◽  
Cluster Analysis ◽  
Developing Countries ◽  
Technology Education ◽  
Hierarchical Cluster ◽  
The Philippines ◽  
Cluster Method ◽  
Development Economic ◽  
Information Technology Education ◽  
It Education

The paper examines worldwide patterns of operations of IT education using 31 countries’ data on IT education focusing on contexts, inputs, processes and outcomes of IT education with the end-in-view of deriving sets of national policies for IT education in the Philippines. In all, 13 variables were used as multivariate inputs to a cluster analysis algorithm which aim to cluster countries in terms of a 13 x 13 similarity matrix utilizing a hierarchical cluster method. Data per variable needed in the cluster analysis were obtained from the internet in most of the countries identified. Results revealed that developing countries’ IT education differed from the IT education of developed and less developed countries in terms of: Contexts (Level of Development, Economic Basis, Educational System), Input (Percent of IT Professionals, National Literacy Rate, Percentage of Universities offering IT Courses), Process (Nature of Tertiary level Curriculum, Number of Years of exposure to IT, Provision of OJT in the curriculum, Instructional system, Admission status of IT courses), Output (Level of IT specialization), and Outcome (Employment status). On the basis of the hierarchical cluster analysis performed, policy recommendations are given to enhance the delivery of IT education in the Philippines and to sharpen its contribution to national development.

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