Research on Shipbuilding Industry Vendor Evaluation Method Based on Data Mining

2015 ◽  
Author(s):  
K Li ◽  
◽  
M Chen ◽  
Y Lin ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Evaluation Method ◽  
Shipbuilding Industry

scholarly journals Integrated Data Mining and TOPSIS Entropy Weight Method to Evaluate Logistics Supply and Demand Efficiency of a 3PL Company

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xintong Yan ◽  
Jian Gong ◽  
Jie He ◽  
Hao Zhang ◽  
Changjian Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Quantitative Methods ◽  
Evaluation Method ◽  
Supply And Demand ◽  
Third Party ◽  
Data Mining Algorithm ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Matching Degree

In the past decades, despite considerable attention having been paid to third-party logistics (3PL) owing to its specialized service, sophisticated operation, and reduced cost, research on quantitative methods for estimating the efficiency of 3PL companies is still lacking, especially for those with small or medium scale. Therefore, the purpose of this study was to establish a quantitative evaluation method to measure the efficiency of the individual nongovernmental 3PL firms and explore the valuable information for the management of 3PL business with Apriori and K-means. Taking TopChains (an emerging nongovernmental 3PL company) as an example, the monthly supply and demand (S&D) level and matching degree were evaluated via the integrating data mining algorithm (i.e., Apriori and K-means) and TOPSIS entropy weight method based on historical data. The findings demonstrate that the S&D level varied with time and space, and the customer demand in February tended to reduce substantially. Besides, the outcome of S&D matching degree in June is undesirable, indicating the unsatisfactory efficiency in resource management. The evaluation maneuver stated in this study can serve as a valuable tool to measure individual nongovernmental 3PL enterprises’ efficiency in terms of S&D, and for reference, the results can aid in rational enterprise investment plan. Besides, this attempt broadened the direction of ARM and K-means being applied in the logistics field.

scholarly journals A Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Meizi Li ◽  
Yang Xiang ◽  
Bo Zhang ◽  
Zhenhua Huang
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Evaluation Method ◽  
Modeling Method ◽  
Chain Model ◽  
Mobile Social Network ◽  
Trust Chain ◽  
Network Platform

User sentiment analysis has become a flourishing frontier in data mining mobile social network platform since the mobile social network plays a significant role in users’ daily communication and sentiment interaction. This study studies the scheme of sentiment estimate by using the users’ trustworthy relationships for evaluating sentiment delivering. First, we address an overview of sentiment delivering estimate scheme and propose its related definitions, that is, trust chain among users, sentiment semantics, and sentiment ontology. Second, this study proposes the trust chain model and its evaluation method, which is composed of evaluation of atomic, serial, parallel, and combined trust chains. Then, we propose sentiment modeling method by presenting its modeling rules. Further, we propose the sentiment delivering estimate scheme from two aspects: explicit and implicit sentiment delivering estimate schemes, based on trust chain and sentiment modeling method. Finally, examinations and results are given to further explain effectiveness and feasibility of our scheme.

scholarly journals Health Evaluation Method of Supercapacitor Based on Data Mining

2021 ◽  
Vol 1861 (1) ◽  
pp. 012006
Author(s):  
Ziwei Fang ◽  
Shuli Liu ◽  
Yanming Zhao
Keyword(s):  
Data Mining ◽  
Evaluation Method ◽  
Health Evaluation

scholarly journals Evaluation Method of IT English Blended Teaching Quality Based on the Data Mining Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lin Shao
Keyword(s):  
Data Mining ◽  
Evaluation Method ◽  
Reference Method ◽  
Teaching Quality ◽  
Quality Of Education ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Mixing Method ◽  
Education Model

As the mixed education model gradually becomes widespread in various universities in Japan, the evaluation of the quality of IT English mixed education has become a very important issue, and it is worth considering the corresponding evaluation method. In this paper, we use a data mining algorithm to implement an evaluation method for the interconversion of quantitative data and qualitative concepts and use the IT English mixed teaching model to evaluate and analyze the teaching quality of the course. The evaluation method is feasible and provides a mixing method. Evaluation of the quality of education. Reference method.

scholarly journals Performance evaluation of random forest with feature selection methods in prediction of diabetes

2020 ◽  
Vol 10 (1) ◽  
pp. 353
Author(s):  
Raghavendra S ◽  
Santosh Kumar J
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Random Forest ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Selection Methods ◽  
Forward Selection ◽  
Data Set ◽  
Prediction Of Diabetes

<p>Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.</p>

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