Personality Classification Experiment by Applying k-Means Clustering

Talasbek, Assem; Serek, Azamat; Zhaparov, Meirambek; Yoo, Seong-Moo; Kim, Young-Kab; Jeong, Geun-Ho

doi:10.3991/ijet.v15i16.15049

Cited by 13 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the initial construction cost is high and the network bandwidth requirements are high. In addition, because the courseware used in the virtual reality teaching process needs to consume a lot of GPU computing resources, the running process of the courseware can only be completed by the local high-performance host, and the desktop virtualization technology cannot be applied [9]. Tian and Zhang proposed that the use of pure B/S structure for design and development can effectively solve this problem [10].…”

Section: Related Workmentioning

confidence: 99%

“…When dealing with large data sets, binary encoding often cannot achieve good encoding effects, so experts and scholars have proposed a new encoding method, floatingpoint encoding, sometimes called truth encoding. e most used floating point number encoding is the decimal floating point number encoding [9]. e data analysis results are used in increased scenarios, and the classification methods relying on previous experience and professional knowledge are difficult to adapt to the current classification needs.…”

Section: Big Data Fuzzy K-means Clustering Teaching Management Systemmentioning

confidence: 99%

See 1 more Smart Citation

Teaching Management System and Algorithm Implementation Based on Big Data Fuzzy K-Means Clustering

Liu

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, the clustering algorithm of big data fuzzy K-means is used to research and analyze the teaching management system. Since the values of the indicator variables of bad units are usually significantly different in smooth areas and discontinuous areas, we cluster the indicators of local areas through K-means clustering, so that only good units or bad units are included in each class. The server is deployed on the campus and is networked with the teaching computers of each classroom. Teachers log in to the class rate management system before or during class, log in to their respective users, select the corresponding class, and make corresponding attendance records according to the attendance of students in the class. The system automatically counts the number of expected arrivals, attendance, late arrivals, leave requests, and absenteeism for the class. Teachers can also fill in information feedback for this class: a list of absenteeism, classroom discipline, student learning status, teaching suggestions, and teaching testimonials. The development environment built by this system is a combination of PHP + MYSQL, which reasonably planned according to the overall needs. The development mode adopts the top-down model, which runs through the whole process from system development to testing to application. The most used floating point number encoding is the decimal floating point number encoding. The relationship of each module of the virtual reality teaching management system is sorted out, the overall framework of the virtual reality teaching management system is established, and the design of the business flow diagram of the management system is completed. This paper also uses Oracle database technology to further explain the design process of designing the system database. Finally, combined with the system design and implementation, the system test is completed from two aspects: system function test and performance test.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Big Data Fuzzy K-means Clustering Teaching Management Systemmentioning

confidence: 99%

Teaching Management System and Algorithm Implementation Based on Big Data Fuzzy K-Means Clustering

Liu

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Every day, people connect verbally and non-verbally in a variety of ways, from casual banter to serious dialogues (Park et al, 2020) such as various social settings like kindergarten, school, college, family, work teams, etc. (Talasbek et al, 2020). Therefore, it is important to identify the personality of the people.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Approach for Identifying Personality Traits based on Individuals' Behavior

Karunarathana

Silva

Rupasingha

2023

SL J. Soc. Sci. Hum.

View full text Add to dashboard Cite

Understanding human personality is essential for natural and social engagement. Arises a significant connection between users' personalities and their behavior. Our primary goal is to identify and classify individuals' personality traits based on their behaviors. Understanding personality types can help better understand preferences and potential differences between them. This study uses users' answers based on the questionnaire on personality to automatically identify the personality type based on behaviors. After pre-processing the data, we researched many classification techniques for automated recognition, including Naive Bayes, Support Vector Machine (SVM), Multilayer Perception (MLP), Random Forest, Logistic, and Decision Tree using a 10-fold cross-validation method. The second observational study combined all the algorithms using an Ensemble Learning algorithm, where by Vote algorithm. Accuracy, precision, recall, f-measure, and error values have been used to measure the systems' performance. According to the comparison analysis, SVM outperforms (85.8%) the other five personality trait detection algorithms. But after combing the five algorithms which contain the highest accuracy by Ensemble Learning algorithm obtained the highest performance (90.5%) than the SVM algorithm and obtained the highest recall, f-measure, and precision values, and the lowest error rates. It demonstrates that an ensemble learning approach that incorporates multiple distinct algorithms may yield greater accuracy than any one of its individual algorithms separately. Our findings are helpful for understanding how to manage and make a relationship with humans by predicting their personality earlier.

show abstract

“…It is found as the best algorithm for the automated creation of a heterogeneous group of learners and is implemented as a Moodle plugin [38]. In another study, K-Means is used to cluster learners' personalities based on the Myers-Briggs Type Indicator (MBTI) [41].…”

Section: Introductionmentioning

confidence: 99%

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Sengkey

Masengi

2023

IJEEI

View full text Add to dashboard Cite

Numerous approaches have been developed to group learners' behavior in an online/blended learning environment. However, most clustering analyses in this particular field only consider numeric features despite the existence of categoric features that are found important in other studies. In this study, we compare K-Means and K-Prototypes algorithms to cluster learners' behavior in a flipped classroom implementation. From the model selection, we found that the model produced by the K-Prototypes algorithmwhich included categoric featuresis a better one. The statistical analysis of the clustering results of the selected K-Prototypes model shows significant differences in most of the inter-cluster comparisons, implying a good separation of the data. More importantly, we can identify the behavior in each cluster which then can be used to help learners in achieving better results in learning.

show abstract

Personality Classification Experiment by Applying k-Means Clustering

Cited by 13 publications

References 11 publications

Teaching Management System and Algorithm Implementation Based on Big Data Fuzzy K-Means Clustering

Teaching Management System and Algorithm Implementation Based on Big Data Fuzzy K-Means Clustering

Ensemble Learning Approach for Identifying Personality Traits based on Individuals' Behavior

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Contact Info

Product

Resources

About