scholarly journals Cluster and Factorial Analysis Applications in Statistical Methods

2021 ◽  
Vol 12 (3) ◽  
pp. 5176-5182
Author(s):  
Ramya Nemani
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Methods ◽  
Research Paper ◽  
Mathematical Technique ◽  
Application Process ◽  
Clustering Techniques ◽  
Factor Rotation ◽  
Analysis Technique ◽  
Factor Modeling

Cluster analysis is a mathematical technique in Multivariate Data Analysis which indicates the proper guidelines in grouping the data into clusters.  We can understand the concept with illustrated notations of cluster Analysis and various Clustering Techniques in this Research paper.  Similarity and Dissimilarity measures and Dendogram Analysis will be computed as required measures for Analysis.  Factor analysis technique is useful for understanding the underlying hidden factors for the correlations among the variables.  Identification and isolation of such facts is sometimes important in several statistical methods in various fields. We can understand the importance of the Factor Analysis and major concept with illustrated Factor Analysis approaches.  We can estimated the Basic Factor Modeling and Factor Loadings, and also Factor Rotation process.  Provides the complete application process and approaches of Principal Factor M.L.Factor and PCA comparison of Factor Analysis in this Research paper

scholarly journals Developing Forecasting Model in Thailand Fashion Market Based on Statistical Analysis and Content-Based Image Retrieval

2017 ◽  
pp. 159-173
Author(s):  
Komaek Kawinakrathiti ◽  
Suphakant Phimoltares ◽  
Patcha Utiswannakul
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Image Retrieval ◽  
High Efficiency ◽  
Content Based Image Retrieval ◽  
Fashion Industry ◽  
Fast Fashion ◽  
Internet Media ◽  
Analysis Technique ◽  
Trend Forecasting

Traditional trend forecasting process in Thailand fashion industry was challenged by a fast fashion. In this paper, the Content-Based Image Retrieval (CBIR) technique is utilized for retrieval of a fashion trendsetter in fast fashion influence. Firstly, six fashion theories were implemented as 12 variables affecting the trendsetter. Cluster analysis, and factor analysis approach were used to find out the source of a fashion trendsetter as well. Cluster analysis separated all samples into three groups with different fashion ways. Moreover, factor analysis technique grouped all variables into three important factors. From such techniques, Internet media clearly is the best source of a fashion trendsetter. In the authors' model, traditional forecasting sources were added up with a fast fashion influence from CBIR. Then, the CBIR was evaluated in terms of efficiency compared with a real fashion expert in the Thai fashion industry. From statistical test, spatial color distribution yields high efficiency in selecting similar fashion style as a fashion expert.

scholarly journals A comparison of three statistical methods applied in the identification of eating patterns

2010 ◽  
Vol 26 (11) ◽  
pp. 2138-2148 ◽  
Author(s):  
Diana Barbosa Cunha ◽  
Renan Moritz Varnier Rodrigues de Almeida ◽  
Rosângela Alves Pereira
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Methods ◽  
Dietary Patterns ◽  
Metropolitan Region ◽  
Eating Patterns ◽  
Reduced Rank Regression ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Traditional Patterns

This work aimed to compare the results of three statistical methods applied in the identification of dietary patterns. Data from 1,009 adults between the ages of 20 and 65 (339 males and 670 females) were collected in a population-based cross-sectional survey in the Metropolitan Region of Rio de Janeiro, Brazil. Information on food consumption was obtained using a semi-quantitative food frequency questionnaire. A factor analysis, cluster analysis, and reduced rank regression (RRR) analysis were applied to identify dietary patterns. The patterns identified by the three methods were similar. The factor analysis identified "mixed", "Western", and "traditional" eating patterns and explained 35% of the data variance. The cluster analysis identified "mixed" and "traditional" patterns. In the RRR, the consumption of carbohydrates and lipids were included as response variables and again "mixed" and "traditional" patterns were identified. Studies comparing these methods can help to inform decisions as to which procedures best suit a specific research scenario.

scholarly journals The New Strategic Imperative: Understanding the Female Business Traveler

2011 ◽  
Vol 8 (11) ◽  
Author(s):  
Francine Newth
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Methods ◽  
Business Strategies ◽  
Business Travelers ◽  
Correlation Analyses ◽  
And Behaviors ◽  
Strategic Imperative ◽  
And Cluster Analysis

This paper explores the characteristics, needs, and behaviors of women who travel on business and analyzes the data for potential segmentation. The study focuses exclusively on the female business traveler. The sample consists of 235 female business travelers from a variety of industries. The statistical methods include correlation analyses, factor analysis, and cluster analysis. The findings show that 6 factors explain 60.4% of the variance in characteristics, behaviors and needs of female business travelers. Cluster analysis further identifies 3 clusters: the Connective, the Empowered, and the Productive. The results show that there are three distinct types of women who travel on business. Strategies are suggested for organizations to use the findings to respond to female business travelers.

scholarly journals Statistical Analyses of Femur Parameters for Designing Anatomical Plates

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lin Wang ◽  
Kunjin He ◽  
Zhengming Chen
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Individual Differences ◽  
Discriminant Analysis ◽  
Statistical Methods ◽  
Proper Class ◽  
The Mean ◽  
Parameter Values ◽  
Bayes Discriminant Analysis

Femur parameters are key prerequisites for scientifically designing anatomical plates. Meanwhile, individual differences in femurs present a challenge to design well-fitting anatomical plates. Therefore, to design anatomical plates more scientifically, analyses of femur parameters with statistical methods were performed in this study. The specific steps were as follows. First, taking eight anatomical femur parameters as variables, 100 femur samples were classified into three classes with factor analysis and Q-type cluster analysis. Second, based on the mean parameter values of the three classes of femurs, three sizes of average anatomical plates corresponding to the three classes of femurs were designed. Finally, based on Bayes discriminant analysis, a new femur could be assigned to the proper class. Thereafter, the average anatomical plate suitable for that new femur was selected from the three available sizes of plates. Experimental results showed that the classification of femurs was quite reasonable based on the anatomical aspects of the femurs. For instance, three sizes of condylar buttress plates were designed. Meanwhile, 20 new femurs are judged to which classes the femurs belong. Thereafter, suitable condylar buttress plates were determined and selected.

Developing Forecasting Model in Thailand Fashion Market Based on Statistical Analysis and Content-Based Image Retrieval

2015 ◽  
Vol 5 (1) ◽  
pp. 32-46
Author(s):  
Komaek Kawinakrathiti ◽  
Suphakant Phimoltares ◽  
Patcha Utiswannakul
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Image Retrieval ◽  
High Efficiency ◽  
Content Based Image Retrieval ◽  
Fashion Industry ◽  
Fast Fashion ◽  
Color Distribution ◽  
Analysis Technique ◽  
Trend Forecasting

Traditional trend forecasting process in Thailand fashion industry was challenged by a fast fashion. In this paper, the Content-Based Image Retrieval (CBIR) technique is utilized for retrieval of a fashion trendsetter in fast fashion influence. Firstly, six fashion theories were implemented as 12 variables affecting the trendsetter. Cluster analysis, and factor analysis approach were used to find out the source of a fashion trendsetter as well. Cluster analysis separated all samples into three groups with different fashion ways. Moreover, factor analysis technique grouped all variables into three important factors. From such techniques, Internet media clearly is the best source of a fashion trendsetter. In the authors' model, traditional forecasting sources were added up with a fast fashion influence from CBIR. Then, the CBIR was evaluated in terms of efficiency compared with a real fashion expert in the Thai fashion industry. From statistical test, spatial color distribution yields high efficiency in selecting similar fashion style as a fashion expert.

Evolution des acides aminés de vins rouges en fonction de la durée de macération

OENO One ◽  
1982 ◽  
Vol 16 (2) ◽  
pp. 111
Author(s):  
Alain Bertrand ◽  
Bernard Médina ◽  
Jean-Pierre Chevallier
Keyword(s):  
Amino Acids ◽  
Cluster Analysis ◽  
Gas Chromatography ◽  
Factor Analysis ◽  
Statistical Methods ◽  
Red Wines ◽  
Analyse Factorielle ◽  
Skin Contact ◽  
Acides Aminés ◽  
Analyse Factorielle Des Correspondances

<p style="text-align: justify;">On étudie l'évolution de la concentration des acides amines de vins rouges (3 cépages) en fonction de la durée de macération. Les analyses sont effectuées par chromatographie en phase gazeuse. Les résultats sont traités par méthodes statistiques : analyse de variance, classification hiérarchique ascendante, analyse factorielle des correspondances.</p><p style="text-align: justify;">+++</p><p style="text-align: justify;">The influence of the time of skin contact of the must on the concentration of amino acids of red wines (Three cultivars) is studied. The analysis are carried out by gas chromatography. Results are treated by statistical methods : variance analysis, cluster analysis (hierarchical ascendant classification), correspondance factor analysis.</p>

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.

Multivariate consumption profiling (MCP) for intelligent meter systems: a methodology to define categories and levels

2010 ◽  
Vol 10 (5) ◽  
pp. 710-720 ◽  
Author(s):  
J. L. Solanas ◽  
M. R. Cussó
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Reference Population ◽  
Statistical Analyses ◽  
Conceptual Schema ◽  
Data Variability ◽  
Water Companies ◽  
Analyse Data

Multivariate Consumption Profiling (MCP) is a methodology to analyse the readings made by Intelligent Meter (IM) systems. Even in advanced water companies with well supported IM, full statistical analyses are not performed, since no efficient methods are available to deal with all the data items. Multivariate Analysis has been proposed as a convenient way to synthesise all IM information. MCP uses Factor Analysis, Cluster Analysis and Discriminant Analysis to analyse data variability by categories and levels, in a cyclical improvement process. MCP obtains a conceptual schema of a reference population on a set of classifying tables, one for each category. These tables are quantitative concepts to evaluate consumption, meter sizing, leakage and undermetering for populations and groupings and individual cases. They give structuring items to enhance “traditional” statistics. All the relevant data from each new meter reading can be matched to the classifying tables. A set of indexes is computed and thresholds are used to select those cases with the desired profiles. The paper gives an example of a MCP conceptual schema for five categories, three variables, and five levels, and obtains its classifying tables. It shows the use of case profiles to implement actions in accordance with the operative objectives.

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