Selection of Alternative Projects Using Data Mining

2007 ◽  
Author(s):  
Sergey Rippa ◽  
Taras Lendyuk
Keyword(s):  
Data Mining ◽  
Using Data ◽  
Selection Of

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

scholarly journals Using Data Mining with C4.5 Algorithm for Student Department Selection at MTs N Kaliangkrik

2021 ◽  
Vol 1 (1) ◽  
pp. 22-36
Author(s):  
Ardhin Primadewi
Keyword(s):  
Data Mining ◽  
Test Score ◽  
General Knowledge ◽  
General Intelligence ◽  
Test Results ◽  
Language Knowledge ◽  
C4.5 Algorithm ◽  
System Input ◽  
Using Data ◽  
Selection Of

Psychological tests can determine the characteristics of behavior, personality, attitudes, interests, motivation, attention, perceptions, thinking power, intelligence, fantasies of students. MTs N Kaliangkrik routinely conducts tests for the selection of majors on its students assisted by Pelita Harapan Bangsa Magelang. In the implementation of the test for students at MTs N Kaliangkrik, processing and calculating the score still used Ms. Excel which requires extra time to recap and know the test results and the school needs to recap the existing results. The system developed applies data mining using the C4.5 Algorithm to predict the selection of majors. The test that is used as system input is the grade IX test score of MTs N Kaliangkrik which includes verbal, non-verbal, general intelligence, language knowledge, definite knowledge, general knowledge, and qualitative power tests. The accuracy of the similarity in the system reaches 80% (good) so that the system is suitable for use as a prediction tool for selecting majors in other schools.

scholarly journals Analysis of acupoint selection and combinations in acupuncture treatment of asthma based on data mining

2021 ◽  
Author(s):  
Pan-Pan Shang ◽  
Cai-Tao Chen ◽  
Mi Cheng ◽  
Yang-Lin Shi ◽  
Yong-Qing Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Acupuncture Treatment ◽  
Interconnection Network ◽  
Rule Mining ◽  
Bladder Meridian ◽  
Using Data ◽  
Conception Vessel ◽  
Selection Of

Objective: Using data mining, the present study aimed to discover the most effective acupoints and combinations in the acupuncture treatment of asthma. Methods: The main acupoints prescribed in these clinical trials was collected and quantified. A network analysis was performed to uncover the interconnections. Additionally, hierarchical clustering analysis and association rule mining were conducted to discover the potential acupoint combinations. Results: Feishu (BL13), Dingchuan (EX-B1), Dazhui (GV14), Shengshu (BL23), Pishu (BL20), and Fengmen (BL12) appeared to be the most frequently used acupoints for asthma. While the Bladder Meridian of Foot Taiyang, the Governor Vessel, and the Conception Vessel, compared to other meridians, were found to be the more commonly selected meridians. In the acupoint interconnection network, Feishu (BL13), Fengmen (BL12), Dingchuan (EX-B1), and Dazhui (GV14) were defined as key node acupoints. Association rule mining analysis demonstrated that the combination of Pishu, Shenshu, Feishu, and Dingchuan, as well as that of Feishu, Dazhui, and Fengmen were potential acupoint combinations that should be selected with priority in asthma treatment. Conclusion: This study provides valuable information regarding the selection of the most effective acupoints and combinations for clinical acupuncture practice and experimental study aimed at the prevention and treatment of asthma.

scholarly journals Penerapan Teknik Clustering Data Mining untuk Memprediksi Kesesuaian Jurusan Siswa (Studi Kasus SMA PGRI 1 Subang)

Faktor Exacta ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 125
Author(s):  
Tubagus Riko Rivanthio ◽  
Mardhiya Ramdhani
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Private School ◽  
Similar Data ◽  
Clustering Techniques ◽  
Academic Achievements ◽  
Processing Data ◽  
Using Data ◽  
Clustering Data ◽  
Selection Of

<p>SMA PGRI 1 Subang is a private school that has several missions, one of which is the establishment of academic and non-academic achievements. In an effort to achieve the mission must supervise student achievement. The effort he did was to provide understanding in the selection of majors in accordance with the interests and talents of students. But in the activity of providing understanding, the school does not yet have a model that can evaluate the interests and talents of students to choose majors. The model can be obtained using student data processing. Data processing can be done using data mining, namely data mining clustering techniques. The technique will produce a model in the selection of majors. This clustering process is the process of grouping similar data based on the similarity of data held by students. The research method used is the CRISP-DM method which has 6 stages consisting of: Business Understanding, Data Understanding, Data Processing, Modeling, Evaluation, and Dissemination. The data that is processed is 620 data consisting of class of students in 2014, 2015, 2016. The results of processing using clustering obtained 6 clusters that have different models for each cluster. The results of this study can be used by schools in recommending courses chosen by students according to students' interests and talents, so students can learn optimally.</p><strong><em>Key words</em></strong>: clustering, dataMining, suitability, majors, students

Mining Big Data in Manufacturing: Requirement Analysis, Tools and Techniques

2014 ◽  
Author(s):  
Utpal Roy ◽  
Bicheng Zhu ◽  
Yunpeng Li ◽  
Heng Zhang ◽  
Omer Yaman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Product Design ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Using Data ◽  
Tools And Techniques ◽  
Audio Video ◽  
Manufacturing Problems ◽  
Selection Of

Data Mining has tremendous potential and usefulness in improving the effectiveness of decision-making in manufacturing. Tools and techniques of data mining can be intelligently applied from product design analysis to the product repair and maintenance. Vast amount of data in the form of documents (text), graphical formats (CAD-file), audio/video, numbers, figures and/or hypertext are available in any typical manufacturing system. Our ultimate goal is to develop data-driven methodologies to solve manufacturing problems using data mining techniques. As a precursor, based on a literature study, this paper investigates selective manufacturing areas to identify the requirements for applying data mining techniques in solving potential manufacturing problems. The reviewed manufacturing areas are: (i) the “Design Intent” retrieval process for the product design and manufacturing, (ii) selection of materials, (iii) performance evaluations of manufacturing process design and operation management, and (iv) product inspection, and after-sales services (repair and maintenance). Industrial efforts towards addressing “Big Data” issues have also been briefly narrated in this paper. Lastly, the paper discusses two important data–related issues that may affect any applications of the data mining tools and techniques — (i) uncertainty involved in data collection, and (ii) interoperability of data collected at different levels of an enterprise.

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