Improvements in the decision making for Cleaner Production by data mining: Case study of vanadium extraction industry using weak acid leaching process

2017 ◽  
Vol 143 ◽  
pp. 582-597 ◽  
Author(s):  
Jia Li ◽  
Yimin Zhang ◽  
Dongyun Du ◽  
Zhengyu Liu
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Acid Leaching ◽  
Weak Acid ◽  
Cleaner Production ◽  
Leaching Process ◽  
Vanadium Extraction

Objective and Subjective Aspects of Decision-Making Support at the Mesoeconomic Level

2016 ◽  
pp. 183-215
Author(s):  
Anastasia Y. Nikitaeva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cognitive Mapping ◽  
Objective Data ◽  
Management Effectiveness ◽  
Cognitive Approach ◽  
Multi Attribute Utility Theory ◽  
On Line ◽  
Analytical Processing

This chapter substantiates the importance of improving management effectiveness of mesoeconomic systems in current economic conditions and the features of mesoeconomy as a management object which defines the high complexity of decision making at the meso level. There are approaches, methods, and technologies which provide support of the decision making process via the integration of formal methods for objective data analysis and methods of accounting to solve semi-structured complex problems of mesoeconomy. A cognitive approach, and an approach involving the integration of the On-Line Analytical Processing and Data mining technologies with methods of a multi-criteria assessment of alternative, in particular methods of Multi-Attribute Utility Theory are considered in the chapter. Cognitive mapping of interaction between state and business in a mesoeconomic system are included as a case-study.

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

Data Mining in Decision Support for Bioenergy Production

2010 ◽  
pp. 1091-1108
Author(s):  
Nasser Ayoub ◽  
Yuji Naka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Tool ◽  
Geographical Information ◽  
Support Tool ◽  
Bioenergy Production ◽  
Decision Making Problem ◽  
Biomass Resources

This chapter presents Data Mining, DM, as a planning and decision support tool for biomass resources management to produce bioenergy. Furthermore, the decision making problem for bioenergy production is defined. A Decision Support System, DSS that utilizes a DM technique, e.g. clustering, integrated with other group of techniques and tools, such as Genetic Algorithms, GA, Life Cycle Assessment, Geographical Information System, GIS, etc, is presented. A case study that shows how to tackle the decision making problem is also shown.

Implementing a Data Mining Solution for an Automobile Insurance Company

2011 ◽  
pp. 63-73 ◽  
Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Competitive Environment ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Data Mining Approach ◽  
Business Data

The insurance company in this case study operates in a highly competitive environment. In recent years it has explored data mining as a means of extracting valuable information from its huge databases in order to improve decision making and capitalise on the investment in business data. This case study describes an investigation into the benefits of data mining for an anonymous Australian automobile insurance company.1 Although the investigation was able to demonstrate quantitative benefits of adopting a data mining approach, there are many practical issues that need to be resolved before the data mining approach can be implemented.

Objective and Subjective Aspects of Decision-Making Support at the Mesoeconomic Level

2020 ◽  
pp. 882-914
Author(s):  
Anastasia Y. Nikitaeva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cognitive Mapping ◽  
Objective Data ◽  
Management Effectiveness ◽  
Cognitive Approach ◽  
Multi Attribute Utility Theory ◽  
On Line ◽  
Analytical Processing

This chapter substantiates the importance of improving management effectiveness of mesoeconomic systems in current economic conditions and the features of mesoeconomy as a management object which defines the high complexity of decision making at the meso level. There are approaches, methods, and technologies which provide support of the decision making process via the integration of formal methods for objective data analysis and methods of accounting to solve semi-structured complex problems of mesoeconomy. A cognitive approach, and an approach involving the integration of the On-Line Analytical Processing and Data mining technologies with methods of a multi-criteria assessment of alternative, in particular methods of Multi-Attribute Utility Theory are considered in the chapter. Cognitive mapping of interaction between state and business in a mesoeconomic system are included as a case-study.

Data Mining in Decision Support for Bioenergy Production

2010 ◽  
pp. 235-253
Author(s):  
Nasser Ayoub ◽  
Yuji Naka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Tool ◽  
Geographical Information ◽  
Support Tool ◽  
Bioenergy Production ◽  
Decision Making Problem ◽  
Biomass Resources

This chapter presents Data Mining, DM, as a planning and decision support tool for biomass resources management to produce bioenergy. Furthermore, the decision making problem for bioenergy production is defined. A Decision Support System, DSS that utilizes a DM technique, e.g. clustering, integrated with other group of techniques and tools, such as Genetic Algorithms, GA, Life Cycle Assessment, Geographical Information System, GIS, etc, is presented. A case study that shows how to tackle the decision making problem is also shown.

Implementing a Data Mining Solution for an Automobile Insurance Company

2006 ◽  
pp. 189-201
Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Competitive Environment ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Data Mining Approach ◽  
Business Data

The insurance company in this case study operates in a highly competitive environment. In recent years it has explored data mining as a means of extracting valuable information from its huge databases in order to improve decision making and capitalise on the investment in business data. This case study describes an investigation into the benefits of data mining for an anonymous Australian automobile insurance company.1 Although the investigation was able to demonstrate quantitative benefits of adopting a data mining approach, there are many practical issues that need to be resolved before the data mining approach can be implemented.

Adapted Visual Analytics Process for Intelligent Decision-Making: Application in a Medical Context

2020 ◽  
Vol 19 (01) ◽  
pp. 241-282 ◽  
Author(s):  
Hela Ltifi ◽  
Emna Benmohamed ◽  
Christophe Kolski ◽  
Mounir Ben Ayed
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Intelligent Decision Making ◽  
The Past ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems ◽  
Intelligent Decision Support

The theoretical and practical researches on Visual Analytics for intelligent decision-making tasks have remarkably advanced in the past few years. Intelligent Decision Support Systems (IDSS) introduce effective and efficient paths from raw data to decision by involving visualization and data mining technologies. Data mining-based DSS produces potentially interesting patterns from data. The transition from extracted patterns to knowledge is a delicate task. In this context, we propose to adapt a common visual analytics process for creating a path that enables the user (decision-maker) to automatically explore and visually extract insights by interacting with the patterns. This proposal is inspired from integrating traditional visual analytics concepts with the mental model of knowledge visualization. The idea is to combine an automatic and visual analysis of patterns to generate knowledge for the purpose of decision-making. To validate our proposal, we have applied it to a medical case study for the fight against Nosocomial Infections in Intensive Care Units. The developed platform was evaluated according to the utility and usability dimensions.

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