This paper introduces the conception of data quality and the issues that the attention has not enough beenpaid to the data quality in data mining (DM). Then, it analyze and emphasize that the data quality is crucial f...This paper introduces the conception of data quality and the issues that the attention has not enough beenpaid to the data quality in data mining (DM). Then, it analyze and emphasize that the data quality is crucial for manyapplications in DM with real examples. Finally an example of the iatric diagnoses application is given to show how toimprove the data quality.展开更多
The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defec...The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defect manufacturing （ZDM） system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-orga- nized decision making system. Data mining （DM） is an effective methodology for discovering interesting knowl- edge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.展开更多
文摘This paper introduces the conception of data quality and the issues that the attention has not enough beenpaid to the data quality in data mining (DM). Then, it analyze and emphasize that the data quality is crucial for manyapplications in DM with real examples. Finally an example of the iatric diagnoses application is given to show how toimprove the data quality.
文摘The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defect manufacturing （ZDM） system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-orga- nized decision making system. Data mining （DM） is an effective methodology for discovering interesting knowl- edge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.
