Online Shopping Customer Data Analysis by Using Association Rules and Cluster Analysis

2013 ◽  
pp. 127-136 ◽  
Author(s):  
Serhat Güden ◽  
Umman Tugba Gursoy
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Association Rules ◽  
Online Shopping ◽  
Customer Data ◽  
And Cluster Analysis

Typology of online shoppers

2003 ◽  
Vol 20 (2) ◽  
pp. 139-156 ◽  
Author(s):  
Ah Keng Kau ◽  
Yingchan E. Tang ◽  
Sanjoy Ghose
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Information Seeking ◽  
Online Shopping ◽  
Demographic Information ◽  
Buying Behavior ◽  
Internet Users ◽  
Online Shoppers ◽  
And Cluster Analysis ◽  
Online Buying

This article aims to examine the online buying behavior among a group of Internet users. Based on a sample of over 3,700 Internet users, this study explores their information‐seeking patterns as well as their motivations and concerns for online shopping. Factor analysis and cluster analysis were used to classify the respondents into six types of online shoppers. Coupled with their demographic information and actual buying behavior, it was possible to constitute a distinct profile for each of the segments. Discriminant analysis was also conducted to seek out the important attitudinal variables that differentiated the various clusters of online shoppers. The implications of such classification are also discussed.

scholarly journals Multivariable Analysis Methods On Identifying Factors and Groups of Students in the Environment of the Discovery Learning/Constructivistic Approach Using Cognitive Tools

2019 ◽  
pp. 7-12
Author(s):  
Konstantinos Korres
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Multivariable Analysis ◽  
Discovery Learning ◽  
Cognitive Tools ◽  
Analysis Methods ◽  
Multiple Variables ◽  
Students Attitudes ◽  
And Cluster Analysis ◽  
The University

This paper studies the environment of the discovery learning/constructivistic approach using cognitive tools regarding students’ performance in tests involving different kinds of learning and in the final formal examinations and students’ attitudes towards the approach in Mathematics’ higher education. In particular the paper aims in identifying factors regarding students’ scores and attitudes affected by the approach and groups of students with similar characteristics based on these factors. Data was obtained by a study realized at the Department of Statistics and Insurance Sciences of the University of Piraeus, concerning the application of the discovery learning/constructivistic approach using Mathematica on the course Calculus (Functions of multiple variables). Multivariable analysis methods are used in the data analysis, in particular factor analysis in identifying factors and cluster analysis in identifying groups of students with similar characteristics, in combination with inferential statistics’ methods. The statistical package SPSS was used for the data analysis.

Data Analysis in Agricultural Experimentation. III. Multiple Comparisons

1993 ◽  
Vol 29 (1) ◽  
pp. 1-8 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Bayesian Methods ◽  
Multiple Comparisons ◽  
Multiple Comparison ◽  
Comparison Methods ◽  
Alternative Approach ◽  
And Cluster Analysis ◽  
Multiple Comparison Methods

SummaryMultiple comparison methods are described. It is noted that they have always been controversial, partly because they emphasize testing at the expense of estimation, partly because they pay no regard to the purpose of the investigation, partly because there are so many competing forms and, not least, because they can lead to illogical conclusions. There are many identified instances where they have been found misleading.An alternative approach is to designate ‘contrasts of interest’ from the start and to concentrate estimation and testing upon them. In many experiments the approach is powerful and definite in use, but sometimes there is no reason to designate one contrast rather than another, for example, in the assessment of new strains or new chemicals. In such circumstances some have found multiple comparisons useful, especially when the problem is to ‘pick the winner’. Bayesian methods and cluster analysis are considered briefly as other alternatives.The current over-use of multiple comparisons is deplored. It is thought to arise in part from bad teaching and in part from the general reluctance of non-statisticians to venture into the unknown territory of specifying contrasts. A bad situation is made worse by the availability of software that carries out multiple comparisons as a matter of course.

MANTiS: a program for the analysis of X-ray spectromicroscopy data

2014 ◽  
Vol 21 (5) ◽  
pp. 1206-1212 ◽  
Author(s):  
Mirna Lerotic ◽  
Rachel Mak ◽  
Sue Wirick ◽  
Florian Meirer ◽  
Chris Jacobsen
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Principal Component ◽  
Analysis Tool ◽  
X Ray ◽  
Manual Analysis ◽  
X Ray Imaging ◽  
Data Analysis Tool ◽  
And Cluster Analysis

Spectromicroscopy combines spectral data with microscopy, where typical datasets consist of a stack of images taken across a range of energies over a microscopic region of the sample. Manual analysis of these complex datasets can be time-consuming, and can miss the important traits in the data. With this in mind we have developedMANTiS, an open-source tool developed in Python for spectromicroscopy data analysis. The backbone of the package involves principal component analysis and cluster analysis, classifying pixels according to spectral similarity. Our goal is to provide a data analysis tool which is comprehensive, yet intuitive and easy to use.MANTiSis designed to lead the user through the analysis using story boards that describe each step in detail so that both experienced users and beginners are able to analyze their own data independently. These capabilities are illustrated through analysis of hard X-ray imaging of iron in Roman ceramics, and soft X-ray imaging of a malaria-infected red blood cell.

scholarly journals Comparing the utilities consumptions in Dubai per category and community: MANOVA and cluster analysis approaches

2021 ◽  
Vol 958 (1) ◽  
pp. 012022
Author(s):  
A Saihi ◽  
A Alzaatreh
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Water Desalination ◽  
Analysis Technique ◽  
Open Government Data ◽  
Government Data ◽  
Increasing Demand ◽  
And Cluster Analysis ◽  
Cluster Analysis Technique ◽  
Desalination Plants

Abstract UAE is marked by the increasing demand for water and electricity due to demographic, environmental and economic factors, coupled with the dependence on water desalination process, which is costly, consumes a lot of energy and is non-environmentally friendly. Like most of the authorities in UAE, Dubai Electricity and Water Authority is facing the challenges of balancing supply with demand and responding to consumer requirements, from one side, and addressing the continuously increasing consumption and slowing it down from another side. Therefore, policy makers can benefit from statistical data analysis in order to make informed decisions. This study aims to equip decision makers with useful tools and analysis to address some of their short- and long-term objectives related to production and consumption. The current study focused on three main objectives: (i) analysing the production of the desalination plants in Dubai, (ii) comparing the consumptions of water and electricity based on the four categories residential, commercial, industrial and others, and (iii) segmenting the various communities in Dubai depending on their consumption behavior. The data used for this study is collected from the open government data and SAS Programming is adopted for data analysis. The results of the analysis revealed that the desalinated water production follows an upward trend, yet still not in line with the consumption growth. Furthermore, there are significant differences between the four categories for both water and electricity consumptions. The highest levels of consumptions are associated with the residential and commercial categories. Finally, the cluster analysis technique revealed fifteen clusters of communities depending on the consumption levels.

scholarly journals Using List of Values as Base of Beer Consumer Segmentation Based on Their Lifestyle-Case of Study City of Pristina

2016 ◽  
Vol 4 (1) ◽  
pp. 171
Author(s):  
Arsim Begunca
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Market Segmentation ◽  
Descriptive Analysis ◽  
Essential Element ◽  
Human Values ◽  
Analysis Program ◽  
Traditional Family ◽  
Consumer Segmentation ◽  
And Cluster Analysis

When studying lifestyles, value is an essential element in defining of internal status for individual. For almost as long as values were studied in consumer’s behavior, methodology was in favor of researchers. Human values have found increasing use as the basis of market segmentation. In our study using the LOV scale as a base for beer consumer segmentation based on lifestyle, we have managed to divide these customers/consumers in three segments. These segments are sports - entertainment segment; traditional-family segment and health-safety segment. Thus several hypotheses generated that have been confirmed. For Data Analysis program named Statistical Package for Social Science ( SPSS version 20) has been used. This study used descriptive analysis, factorial analysis, independent samples, T-test, one way between groups ANOVA and cluster analysis.

The limit of the contemporary financial system in Russia

2020 ◽  
Vol 16 (7) ◽  
pp. 1223-1245
Author(s):  
V.V. Smirnov
Keyword(s):  
Cluster Analysis ◽  
Foreign Currency ◽  
Financial System ◽  
Descriptive Statistics ◽  
High Tech ◽  
Monetary Aggregate ◽  
Internal System ◽  
Monetary Relations ◽  
And Cluster Analysis ◽  
Structural Preservation

Subject. The article focuses on the modern financial system of Russia. Objectives. I determine the limit of the contemporary financial system in Russia. Methods. The study is based on methods of descriptive statistics, statistical and cluster analysis. Results. The article shows the possibility of determining the scope of the contemporary financial system in Russia by establishing monetary relations as the order of the internal system and concerted operation of subsystems, preserving the structure of the financial system, maintaining the operational regime, implementing the program and achieving the goal. I found that the Russian financial system correlated with the Angolan one, and the real scope of the contemporary financial system in Russia. Conclusions and Relevance. As an attempt to effectively establish monetary relations and manage them, the limit of the contemporary financial system is related to the possibility of using Monetary Aggregate M0 to maintain the balance of the Central Bank of Russia. To overcome the scope of Russia’s financial system, the economy should have changed its specialization, refocusing it on high-tech export and increasing the foreign currency reserves. This can be done if amendments to Russia’s Constitution are adopted. The findings expand the scope of knowledge and create new competence in the establishment of monetary relations, order of the internal system and concerted interaction of subsystems, structural preservation of the financial system and maintenance of its operational regime.

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