Introduction to Data Mining and its Applications to Manufacturing

2008 ◽  
pp. 146-168 ◽  
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

Introduction to Data Mining and its Applications to Manufacturing

2011 ◽  
pp. 144-166
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

Expanding Business Intelligence Power with System Dynamics

2011 ◽  
pp. 126-140 ◽  
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
System Dynamics ◽  
Real World ◽  
Business Intelligence ◽  
Simulation And Modeling ◽  
Business World ◽  
Data Mining Techniques ◽  
Business Dynamics ◽  
Using Data ◽  
Systems Thinking Approach

This chapter explores the opportunities to expand the forecasting and business understanding capabilities of Business Intelligence (BI) tools with the support of the system dynamics approach. System dynamics tools can enhance the insights provided by BI applications — specifically by using data-mining techniques, through simulation and modeling of real world under a “systems thinking” approach, improving forecasts, and contributing to a better understanding of the business dynamics of any organization. Since there is not enough diffusion and understanding in the business world about system dynamics concepts and advantages, this chapter is intended to motivate further research and the development of better and more powerful applications for BI.

Study on the Application of Data Mining Based on Campus Card Platform

2013 ◽  
Vol 846-847 ◽  
pp. 977-980 ◽  
Author(s):  
Yuan Qian ◽  
Quan Shi
Keyword(s):  
College Students ◽  
Data Mining ◽  
Time Series ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Sql Server ◽  
Policy Makers ◽  
University Policy ◽  
Development Tools ◽  
Intelligence Development

The thesis uses data in the database of campus card platform as the analysis object, combined with statistical methods and data mining technology to analyze the students consumption and the situation of the canteens. We use the Microsoft .NET and SQL Server 2008 business intelligence development tools to mine and analyze these data; know canteens consumption and learn about the business status and the popular shops of the canteen by using the K-means algorithm; analyze and predict students behavior and the situation of the canteen by using time series algorithm. It is convenient to manage the college students, and provide data support for university policy makers and shoppers to make plans.

scholarly journals Prediction of Tropical Cyclones’ Characteristic Factors on Hainan Island Using Data Mining Technology

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ruixu Zhou ◽  
Wensheng Gao ◽  
Bowen Zhang ◽  
Xianggan Fu ◽  
Qinzhu Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Wind Speed ◽  
Tropical Cyclones ◽  
Statistical Methods ◽  
Average Distance ◽  
Hainan Island ◽  
Mining Technology ◽  
Accurate Judgment ◽  
Combining Data ◽  
Using Data

A new methodology combining data mining technology with statistical methods is proposed for the prediction of tropical cyclones’ characteristic factors which contain latitude, longitude, the lowest center pressure, and wind speed. In the proposed method, the best track datasets in the years 1949~2012 are used for prediction. Using the method, effective criterions are formed to judge whether tropical cyclones land on Hainan Island or not. The highest probability of accurate judgment can reach above 79%. With regard to TCs which are judged to land on Hainan Island, related prediction equations are established to effectively predict their characteristic factors. Results show that the average distance error is improved compared with the National Meteorological Centre of China.

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