scholarly journals Approach to Integration of Data Mining Techniques in Simulation Results Analysis

2018 ◽  
Vol 21 ◽  
pp. 86-92
Author(s):  
Irīna Šitova ◽  
Jelena Pečerska
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Theoretical Study ◽  
Queueing System ◽  
Decision Rules ◽  
Data Mining Techniques ◽  
Simulation Results ◽  
Using Data ◽  
Two Stages

The research is carried out in the area of analysis of simulation results by using data mining techniques. The goal of the research is to explore the applicability of data mining techniques in the area of simulation results analysis, to offer an application scheme of data mining techniques in the analysis of simulation results, as well as to demonstrate the usage of these techniques in the analysis of experimental data. As a result of the theoretical study, an approach is proposed, consisting of two stages and combining the fundamental techniques of data farming and knowledge discovery. A variety of data mining techniques, such as correlation analysis, clustering and several visualization mechanisms of results, are used for knowledge discovery. The proposed approach is applied to the analysis of experimental data. The performance of a queueing system is analysed, and knowledge and decision rules are obtained from simulation results.

Data Mining Techniques in Agricultural and Environmental Sciences

2012 ◽  
pp. 311-325 ◽  
Author(s):  
Altannar Chinchuluun ◽  
Petros Xanthopoulos ◽  
Vera Tomaino ◽  
P.M. Pardalos
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Future Directions ◽  
Data Mining Techniques ◽  
Useful Knowledge ◽  
Future Developments ◽  
Large Sets ◽  
Agriculture And Environment ◽  
Advanced Knowledge ◽  
Using Data

Data mining techniques are largely used in different sectors of the economy and they increasingly are playing an important role in agriculture and environment-related areas. This paper aims to show our vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment. Efforts for searching hidden patterns in data are not a recent phenomenon. History shows that extensive observations on data have helped discover empirical laws in different fields of research. Therefore, it is important to provide researchers in agriculture and environmental-related areas with the most advanced knowledge discovery techniques. Data mining is the process of extracting important and useful information from large sets of data. This information can be converted into useful knowledge that could help to better understand the problem in study and to better predict future developments. The paper presents the state of the art in data mining and knowledge discovery techniques and provides discussions for future directions.

Exploration of Healthcare Using Data Mining Techniques

2018 ◽  
pp. 243-259 ◽  
Author(s):  
Anindita Desarkar ◽  
Ajanta Das
Keyword(s):  
Data Mining ◽  
Patient Care ◽  
Knowledge Discovery ◽  
Healthcare Cost ◽  
Healthcare Sector ◽  
Huge Amount ◽  
Data Set ◽  
Data Mining Techniques ◽  
Using Data ◽  
Tools And Techniques

Huge amount of data is generated from Healthcare transactions where data are complex, voluminous and heterogeneous in nature. This large dataset can be used as an ideal store which can be analyzed for knowledge discovery as well as various future predictions. So, Data mining is becoming increasingly popular as it offers set of innovative tools and techniques to handle this kind of data set whereas traditional methods have limitations for that. In summary, providing the better patient care and reduction in healthcare cost are two major goals of application of data mining in healthcare. Initially, this chapter explores on the various types of eHealth data and its characteristics. Subsequently it explores various domains in healthcare sector and shows how data mining plays a major role in those domains. Finally, it describes few common data mining techniques and their applications in eHealth domain.

Drug Prediction System Using Data Mining Techniques - A Survey

2018 ◽  
pp. 32-43
Author(s):  
Jagadeesan V. ◽  
Dr. Palanivel K
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Learning Strategies ◽  
Clinical Decision Making ◽  
Machine Learning Algorithms ◽  
Knowledge Discovery In Databases ◽  
Treatment Quality ◽  
Mining Equipment ◽  
Data Mining Techniques ◽  
Using Data

The thriving Medical applications of Data mining in the fields of Medicine and Public health has led to the popularity of its use in Knowledge Discovery in Databases (KDD). Data mining has revealed novel Biomedical and Healthcare acquaintances for Clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Drug Prediction is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of Knowledge discovery in Databases using Data mining techniques that are in use in today’s Medical research. To enable the drug retrieval and the breakthrough of hidden retrieval patterns from related databases, a study is made. Also, the use of data mining to discover such relationships as those between Supervised and Unsupervised are presented. This paper summarizes various Machine learning algorithms based on various Data mining techniques in learning strategies. It has also been targeted on contemporary research being done the usage of the Data mining strategies to beautify the retrieval manner. This research paper offers destiny developments of modern-day strategies of KDD, using data mining equipment for medicinal drug industry. It also confers huge troubles and demanding situations related to information mining and medication area. The research discovered a developing quantity of records mining packages, such as evaluation of drugs names for higher fitness policy-making, detection of accurate effects with outbreaks and preventable from misclassified drug names.

Data Mining Techniques in Agricultural and Environmental Sciences

2010 ◽  
Vol 1 (1) ◽  
pp. 26-40 ◽  
Author(s):  
Altannar Chinchuluun ◽  
Petros Xanthopoulos ◽  
Vera Tomaino ◽  
P.M. Pardalos
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Future Directions ◽  
Data Mining Techniques ◽  
Useful Knowledge ◽  
Large Sets ◽  
Agriculture And Environment ◽  
Advanced Knowledge ◽  
Hidden Patterns ◽  
Using Data

Data mining techniques are largely used in different sectors of the economy and they increasingly are playing an important role in agriculture and environment-related areas. This paper aims to show our vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment. Efforts for searching hidden patterns in data are not a recent phenomenon. History shows that extensive observations on data have helped discover empirical laws in different fields of research. Therefore, it is important to provide researchers in agriculture and environmental-related areas with the most advanced knowledge discovery techniques. Data mining is the process of extracting important and useful information from large sets of data. This information can be converted into useful knowledge that could help to better understand the problem in study and to better predict future developments. The paper presents the state of the art in data mining and knowledge discovery techniques and provides discussions for future directions.

scholarly journals KNOWLEDGE DISCOVERY USING DATA MINING TECHNIQUES: A CASE STUDY

2010 ◽  
Vol XIX (IX), 2010/3 (3) ◽  
Author(s):  
OPREA Cristina ◽  
ZAHARIA Marian ◽  
ENÄCHESCU Daniela
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Data Mining Techniques ◽  
Using Data

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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