The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transf...The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.展开更多
Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in ...Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in enterprises, the product lifecycle data have been effectively managed. However, these data have not been fully utilized in module division, especially for complex machinery products. To solve this problem, a product module mining method for the PLM database is proposed to improve the effect of module division. Firstly, product data are extracted from the PLM database by data extraction algorithm. Then, data normalization and structure logical inspection are used to preprocess the extracted defective data. The preprocessed product data are analyzed and expressed in a matrix for module mining. Finally, the fuzzy c-means clustering(FCM) algorithm is used to generate product modules, which are stored in product module library after module marking and post-processing. The feasibility and effectiveness of the proposed method are verified by a case study of high pressure valve.展开更多
With the rapid development of the Internet, market has been increasingly competitive and competition means are various. Internet Marketing has become a new way for enterprises to grow. Data mining of enterprise networ...With the rapid development of the Internet, market has been increasingly competitive and competition means are various. Internet Marketing has become a new way for enterprises to grow. Data mining of enterprise network marketing has become the new darling of many business managers.The marketing data will become a key to develop a corporate marketing strategy as an important basis tool. However, many companies now make mistakes in marketing data. They can＇t fully exploit the marketing data.lt can affect the development of enterprise network marketing strategy. This paper is based on the concept of marketing data and outline the importance of data mining for network marketing, then analyze a significant impact on the entemrise network marketin~ strate^w made by marketinR data mininR.展开更多
This case concerns the evaluation of a capital investment and provides an opportunity to conduct a sensitivity analysis of outcomes based on alternative project assumptions. Optimum production outputs depend on a reli...This case concerns the evaluation of a capital investment and provides an opportunity to conduct a sensitivity analysis of outcomes based on alternative project assumptions. Optimum production outputs depend on a reliable fleet of minesite vehicles. Replacement and maintenance alternatives need to be assessed and managed to ensure effective outcomes.展开更多
This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM al...This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.展开更多
Data mining enables us to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive adva...Data mining enables us to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive advantage by data mining methods. Data mining is used in different fields. In finance field, it is a specially used in portfolio management, fraud detection, payment prediction, loan risk analysis, mortgage scoring, determining transaction manipulation, determining financial risk management, determining customer profile and foreign exchange market. It can be costly, risky and time consuming for enterprises to gain knowledge. Thus today enterprises use data mining as an innovative competitive mean. The aim of the study is to determine the importance of data mining in financial applications.展开更多
文摘The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.
基金Project(51275362)supported by the National Natural Science Foundation of ChinaProject(2013M542055)supported by China Postdoctoral Science Foundation Funded
文摘Modular technology can effectively support the rapid design of products, and it is one of the key technologies to realize mass customization design. With the application of product lifecycle management(PLM) system in enterprises, the product lifecycle data have been effectively managed. However, these data have not been fully utilized in module division, especially for complex machinery products. To solve this problem, a product module mining method for the PLM database is proposed to improve the effect of module division. Firstly, product data are extracted from the PLM database by data extraction algorithm. Then, data normalization and structure logical inspection are used to preprocess the extracted defective data. The preprocessed product data are analyzed and expressed in a matrix for module mining. Finally, the fuzzy c-means clustering(FCM) algorithm is used to generate product modules, which are stored in product module library after module marking and post-processing. The feasibility and effectiveness of the proposed method are verified by a case study of high pressure valve.
文摘With the rapid development of the Internet, market has been increasingly competitive and competition means are various. Internet Marketing has become a new way for enterprises to grow. Data mining of enterprise network marketing has become the new darling of many business managers.The marketing data will become a key to develop a corporate marketing strategy as an important basis tool. However, many companies now make mistakes in marketing data. They can＇t fully exploit the marketing data.lt can affect the development of enterprise network marketing strategy. This paper is based on the concept of marketing data and outline the importance of data mining for network marketing, then analyze a significant impact on the entemrise network marketin~ strate^w made by marketinR data mininR.
文摘This case concerns the evaluation of a capital investment and provides an opportunity to conduct a sensitivity analysis of outcomes based on alternative project assumptions. Optimum production outputs depend on a reliable fleet of minesite vehicles. Replacement and maintenance alternatives need to be assessed and managed to ensure effective outcomes.
文摘This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.
文摘Data mining enables us to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive advantage by data mining methods. Data mining is used in different fields. In finance field, it is a specially used in portfolio management, fraud detection, payment prediction, loan risk analysis, mortgage scoring, determining transaction manipulation, determining financial risk management, determining customer profile and foreign exchange market. It can be costly, risky and time consuming for enterprises to gain knowledge. Thus today enterprises use data mining as an innovative competitive mean. The aim of the study is to determine the importance of data mining in financial applications.
