An Unsupervised Clustering Approach for Twitter Sentimental Analysis: A Case Study for George Floyd Incident

2020 ◽  
Vol 68 (6) ◽  
pp. 46-50
Author(s):  
Balaji Karumanchi
Keyword(s):  
Unsupervised Clustering ◽  
Clustering Approach

Course Design Based on Students’ English Skill Cluster: A Case Study in a University Language Center

Jurnal METRIS ◽  
2020 ◽  
Vol 21 (02) ◽  
pp. 67-71
Author(s):  
Eldwin Ilham Murpratomo ◽  
Amelia Kurniawati ◽  
Hilman Dwi Anggana
Keyword(s):  
Foreign Language ◽  
Course Design ◽  
Course Materials ◽  
Post Test ◽  
Prediction Test ◽  
Clustering Approach ◽  
Average Cluster ◽  
Cluster 2 ◽  
Different Levels

The English Proficiency Test (EPrT) is a prediction test for English as a Foreign Language (TOEFL), which is a prerequisite for graduation at XYZ University. The Language Center provides a course for EPrT preparation. The course posttest data shows that only 74% of students met the graduation prerequisites. This study aims to develop an English course design based on the students’ English skill cluster. This study uses the K-Means clustering approach to classify the students based on English skills. The respondents are 397 students who joined the EPrT preparation course in October and November 2018. The 397 students are distributed into 3 clusters, which are 174 students in cluster 1, 116 students in cluster 2, and 107 students in cluster 3. Cluster 1 consists of students with the score below average. Cluster 2 consists of students with the total score above average, but the components score is below average. Cluster 3 consists of students with pre-test total score below average, but the post-test score are above average. Therefore, the EPrT preparation course is suggested to have different levels, instead of one level as now. The course materials are designed to be suitable for students’ initial English skills at each level.

An unsupervised clustering approach for leukaemia classification based on DNA micro-arrays data

2007 ◽  
Vol 11 (2) ◽  
pp. 175-188 ◽  
Author(s):  
Simone Garatti ◽  
Sergio Bittanti ◽  
Diego Liberati ◽  
Andrea Maffezzoli
Keyword(s):  
Unsupervised Clustering ◽  
Clustering Approach

An unsupervised clustering approach for yield stress prediction during flat rolling

2012 ◽  
Vol 45 (23) ◽  
pp. 50-55 ◽  
Author(s):  
Francesco A. Cuzzola ◽  
Claudio Aurora ◽  
Daniele Sclauzero
Keyword(s):  
Yield Stress ◽  
Unsupervised Clustering ◽  
Flat Rolling ◽  
Stress Prediction ◽  
Clustering Approach

NEW DRUG R&D OF TRADITIONAL CHINESE MEDICINE: ROLE OF DATA MINING APPROACHES

2009 ◽  
Vol 17 (03) ◽  
pp. 329-347 ◽  
Author(s):  
HONGJUN YANG ◽  
JIANXIN CHEN ◽  
SHIHUAN TANG ◽  
ZHENKUN LI ◽  
YISONG ZHEN ◽  
...  
Keyword(s):  
Data Mining ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Unsupervised Clustering ◽  
Training Set ◽  
Regression Methods ◽  
New Drug ◽  
Clustering Approach

Traditional Chinese Medicine (TCM) documented about 100,000 formulae during past 2500 years. To use and customize them by modern pharmaceutical industry, we make an interdisciplinary effort to study the activity of new drug research and development (R&D) in TCM by introducing data mining approaches to it. We used the migraine formulae as a training set to investigate the possibility of developing new prescription by means of data mining. The activity of new drug R&D of TCM consists of two steps. The first step is to discover new prescriptions (candidates for drugs) from migraine formulae. We present an unsupervised clustering approach based on data mining theory to address the problem in the first step and automatically discover ten new prescriptions from the formulae data. The second step is to develop and optimize the prescriptions discovered by current biomedical approaches. Since Ligusticum chuanxiong Hort (LCH), a kind of herb, is often used to treat migraine and appears in the new prescriptions, we use it as an example and apply supervised regression method based on data mining theory to study the drug R&D activity of TCM. We revised two linear regression methods in order to establish the nonlinear association between three chemical ingredients of LCH and corresponding pharmacological activity and used it to predict the activities. The association is validated by in vitro experiments and we found that the experimental results are consistent with the prediction. Unsupervised clustering and supervised regression cover most part of data mining theory, which means that data mining approaches play a crucial role in new drug R&D in TCM and present a better solution to establish the platform of drug R&D in TCM.

Engineering Achievement and Application of Unascertained Clustering Decision Algorithm Based on MATLAB

2010 ◽  
Vol 26-28 ◽  
pp. 585-590
Author(s):  
Shu Sheng Sun ◽  
Wei Wu
Keyword(s):  
Unsupervised Clustering ◽  
Specific Procedure ◽  
Decision Algorithm

Unascertained clustering is an unsupervised clustering based on unascertained set. It is a method to make soft division of objects which is more scientific and realistic than the general one. According to the basic idea of unascertained average clustering, we write a specific procedure to achieve calculation by the MATLAB technology and then we verify the feasibility and effectiveness of the written procedures as well as the practical value and feasibility of calculation through case study.

A framework for strategy formulation based on clustering approach: A case study in a corporate organization

2013 ◽  
Vol 49 ◽  
pp. 37-49 ◽  
Author(s):  
S. Ali Hadighi ◽  
Navid Sahebjamnia ◽  
Iraj Mahdavi ◽  
Mohsen Akbarpour Shirazi
Keyword(s):  
Strategy Formulation ◽  
Corporate Organization ◽  
Clustering Approach

scholarly journals Two-Stage Geothermal Well Clustering for Oil-to-Water Conversion on Mature Oil Fields

Geosciences ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 470
Author(s):  
Josipa Hranić ◽  
Sara Raos ◽  
Eric Leoutre ◽  
Ivan Rajšl
Keyword(s):  
Spatial Arrangement ◽  
Oil Wells ◽  
Oil Field ◽  
Oil Fields ◽  
Two Stage ◽  
Oil Companies ◽  
Geothermal Potential ◽  
Clustering Approach ◽  
Water Cut

There are numerous oil fields that are approaching the end of their lifetime and that have great geothermal potential considering temperature and water cut. On the other hand, the oil industry is facing challenges due to increasingly stringent environmental regulations. An example of this is the case of France where oil extraction will be forbidden starting from the year 2035. Therefore, some oil companies are considering switching from the oil business to investing in geothermal projects conducted on existing oil wells. The proposed methodology and developed conversions present the evaluation of existing geothermal potentials for each oil field in terms of water temperature and flow rate. An additional important aspect is also the spatial distribution of existing oil wells related to the specific oil field. This paper proposes a two-stage clustering approach for grouping similar wells in terms of their temperature properties. Once grouped on a temperature basis, these clusters should be clustered once more with respect to their spatial arrangement in order to optimize the location of production facilities. The outputs regarding production quantities and economic and environmental aspects will provide insight into the optimal scenario for oil-to-water conversion. The scenarios differ in terms of produced energy and technology used. A case study has been developed where the comparison of overall fields and clustered fields is shown, together with the formed scenarios that can further determine the possible conversion of petroleum assets to a geothermal assets.

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