scholarly journals Data mining Application of Data Reduction and Clustering Domain of Textile Database

2020 ◽  
Vol 9 (4) ◽  
pp. 228-232
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Data Structure ◽  
Factor Scores ◽  
Varimax Rotation ◽  
Textile Fabrics ◽  
Number Of Factors ◽  
Rotation Methods ◽  
Variation Explained ◽  
Data Mining Application

This research paper attempts to identify the textile data structure and hidden pattern of original database with certain important parameters. The main objectives of this study are to identify the first n number of factors that explained over the study period. Initially factor analysis is performed to extract factor scores. Principal extraction is performed through Data mining package with sixteen textile fabrics parameters. Factor extraction is aimed to uncover the intrinsic pattern among the textile parameters considered and an important point of factor analysis is to extract factor scores for further investigation. Thus, factor analysis consistently resulted in three factors for the whole datasets. The amount of total variation explained is over 75 percent in factor analysis with varimax rotation. The factor loadings or factor structure matrix with unassociated rotation methods are not always easy to interpret. The nonhierarchical kmean clustering is also used to identify meaningful cluster based on their parameter means of original database.

Factor Analysis in Discriminating the Racial Origin of Sockeye Salmon (Oncorhynchus nerka)

1974 ◽  
Vol 31 (1) ◽  
pp. 1-10 ◽  
Author(s):  
H. B. Messinger ◽  
H. T. Bilton
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Factor Scores ◽  
Discriminant Functions ◽  
Varimax Rotation ◽  
Racial Origin ◽  
Usual Procedure ◽  
New Variables

A comparison was made between the procedure of factoring variables before using them in discriminant analysis and the usual procedure of using the original variables in discriminant analysis. The results indicated factoring seven scale measurements on sockeye salmon (Oncorhynchus nerka) with varimax rotation produced four new variables which gave more valid results in classifying sockeye salmon by area of origin than the original variables when discriminant functions were computed. Although the results on the basic data from which the functions were derived were not as good using the four factor scores as the seven original variables, the accuracy of classification was much more consistent in the test data with the factor scores. The loss in accuracy was at least [Formula: see text] times as great for functions based on the original variables as for ones based on factor scores. The errors in classifying fish to their individual places of origin were perhaps too large for the procedure to be useful in the field, but the accuracy of classification to the British Columbia or Alaska region was quite high.

scholarly journals The identification and perceived importance of dimensions of managerial success for different levels of management

1995 ◽  
Vol 21 (2) ◽  
Author(s):  
M. P. Loubser ◽  
L. C. De Jager
Keyword(s):  
Factor Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Relative Importance ◽  
Factor Scores ◽  
Varimax Rotation ◽  
Managerial Success ◽  
Perceived Importance ◽  
Different Levels ◽  
Multiple Analysis

Dimensions or factors related to managerial success were identified from the literature and a list of 78 generic dimensions compiled. These dimensions were rated in terms of their relative importance for every level of management by 241 managers on junior, middle and senior levels. A principal components factor analysis with varimax rotation was performed on the data and nine factors or clusters of dimensions were extracted. The resulting factor scores were then subjected to a multiple analysis of variance. Results indicate that the importance of these factors differ significantly across the three levels of management. The implications of the findings are discussed in both theoretical and practical terms. Opsomming Dimensies of faktore wat met bestuursukses verband hou is uit die literatuur gei'dentifiseer en 'n lys van 78 generiese dimensies is saamgestel. Hierdie dimensies se relatiewe belangrikheid vir eike bestuursvlak is deur 241 bestuurders op junior, middel en senior vlak beoordeel. 'n Hoofkomponent faktorontleding met varimax rotasie is op die data uitgevoer en nege faktore of groepe dimensies is onttrek. Die resulterende faktortellings is daarna aan 'n meervoudige analise van variansie onderwerp. Resultate dui daarop dat die belangrikheid van hierdie faktore beduidend verskil oor die verskillende bestuursvlakke. Die implikasies van die bevindinge word in beide teoretiese en praktiese terme bespreek.

scholarly journals Identification of the relationship between some characteristics of native walnut genotypes peculiar to Darende district of Malatya province: Use of factor analysis scores in multiple linear regression

Genetika ◽  
2016 ◽  
Vol 48 (3) ◽  
pp. 923-932 ◽  
Author(s):  
Omer Beyhan ◽  
Ecevit Eyduran ◽  
Meleksen Akin ◽  
Sezai Ercisli ◽  
Kenan Gecer ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Extraction Method ◽  
Principal Component ◽  
Kernel Weight ◽  
Eastern Anatolia ◽  
Factor Scores ◽  
Varimax Rotation ◽  
Independent Variables ◽  
Selection Strategies ◽  
The Relationship

Two main aims of this investigation were to predict kernel ratio (KR) and kernel weight (KW) from some walnut characteristics, respectively. For these aims, a total of 112 Walnut genotypes growing in nature were collected at Darende District of Malatya province in the Eastern Anatolia region of Turkiye. The walnut characteristics evaluated were nut length (NL), nut width (NW), nut height (NH), nut weight (NWe), shell thickness (ST), kernel ratio (KR) and kernel weight (KW), respectively. Independent variables were subjected to factor analysis based on principal component extraction method and VARIMAX rotation. On the basis of jointly using factor scores in multiple regression, KR (81.3 % R2 and 80.6 % adjusted R2) and KW (94.7% R2 and 94.5% adjusted R2) characteristics were predicted by using four factor scores with a big accuracy without multicollinearity problem. Consequently, the present results revealed that, walnuts of heavier KW and NWe in the prediction of KR would be expected to produce those of higher KR, and walnuts of higher values in NH, NW, NWe, ST, NL, and KR in the prediction of KW would be expected to produce those of heavier KW. The knowledge may help walnut breeders to improve new selection strategies.

The Structure Among Measures of Personality, Social Attitudes, Values, and Social Norms

2007 ◽  
Vol 28 (4) ◽  
pp. 240-251 ◽  
Author(s):  
Lazar Stankov
Keyword(s):  
Factor Analysis ◽  
Individual Differences ◽  
Social Norms ◽  
Social Interactions ◽  
Correlation Matrix ◽  
Social Attitudes ◽  
Group Differences ◽  
Factor Scores ◽  
Ethnic Group Differences ◽  
Inside Out

Abstract. This paper presents the results of a study that employed measures of personality, social attitudes, values, and social norms that have been the focus of recent research in individual differences. These measures were given to a sample of participants (N = 1,255) who were enrolled at 25 US colleges and universities. Factor analysis of the correlation matrix produced four factors. Three of these factors corresponded to the domains of Personality/Amoral Social Attitudes, Values, and Social Norms; one factor, Conservatism, cut across the domains. Cognitive ability showed negative correlation with conservatism and amoral social attitudes. The study also examined gender and ethnic group differences on factor scores. The overall interpretation of the findings is consistent with the inside-out view of human social interactions.

A Comparison of Simple Structure Rotation Criteria in Temporal Exploratory Factor Analysis for Event-Related Potential Data

Methodology ◽  
2019 ◽  
Vol 15 (Supplement 1) ◽  
pp. 43-60 ◽  
Author(s):  
Florian Scharf ◽  
Steffen Nestler
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Simple Structure ◽  
Ground Truth ◽  
Event Related Potential ◽  
Suitable Alternative ◽  
Factor Rotation ◽  
Rotation Methods ◽  
Simple Structure Rotation ◽  
Temporal Overlap

Abstract. It is challenging to apply exploratory factor analysis (EFA) to event-related potential (ERP) data because such data are characterized by substantial temporal overlap (i.e., large cross-loadings) between the factors, and, because researchers are typically interested in the results of subsequent analyses (e.g., experimental condition effects on the level of the factor scores). In this context, relatively small deviations in the estimated factor solution from the unknown ground truth may result in substantially biased estimates of condition effects (rotation bias). Thus, in order to apply EFA to ERP data researchers need rotation methods that are able to both recover perfect simple structure where it exists and to tolerate substantial cross-loadings between the factors where appropriate. We had two aims in the present paper. First, to extend previous research, we wanted to better understand the behavior of the rotation bias for typical ERP data. To this end, we compared the performance of a variety of factor rotation methods under conditions of varying amounts of temporal overlap between the factors. Second, we wanted to investigate whether the recently proposed component loss rotation is better able to decrease the bias than traditional simple structure rotation. The results showed that no single rotation method was generally superior across all conditions. Component loss rotation showed the best all-round performance across the investigated conditions. We conclude that Component loss rotation is a suitable alternative to simple structure rotation. We discuss this result in the light of recently proposed sparse factor analysis approaches.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

scholarly journals Inequalities in the Exercise and Continuity in Building Engineering in Spain. Factor Analysis Including Gender Perspective

2021 ◽  
Vol 13 (10) ◽  
pp. 5514
Author(s):  
Irantzu Recalde-Esnoz ◽  
Daniel Ferrández ◽  
Carlos Morón ◽  
Guadalupe Dorado
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Gender Inequality ◽  
Sustainable Development Goals ◽  
The Sustainable Development ◽  
Agenda 2030 ◽  
Technical Architecture ◽  
Development Goals ◽  
Building Engineering ◽  
New Jobs

The building sector is one of the most relevant at world level in view of the percentage of gross domestic product (GDP) concerned, as well as the number of new jobs created. Nevertheless, it is a completely male-dominated industry. Different institutions and organisms, such as the Agenda 2030 and the Sustainable Development Goals, struggle to reduce gender inequality in different environments, including the working one. Aligned with these goals, this study provides the data exploited from the first survey regarding gender inequality within the professionals of the building engineering field in the Spanish population as a whole. This survey was developed in 2018 by the Spanish General Council of Technical Architecture and it was sent to its members. The sample involved 1353 cases. For this data mining, bivariate analyses were conducted in order to subsequently carry out a factor analysis and the socio–demographic composition of the dimensions found. Results exposed statistically meaningful differences in the eyes of women and men about those factors which facilitate practice and continuity in the profession. The most relevant conclusions drawn from the factor analysis reflect the existence of three factors: (1) work competences, (2) social capital and (3) physical appearance and being a man, dimensions in which women and men’s opinion was unevenly distributed.

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