M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen
{"title":"数据预处理","authors":"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen","doi":"10.1093/oso/9780192897879.003.0002","DOIUrl":null,"url":null,"abstract":"This chapter focuses on the process of cleaning data and preparing it for further processing. Specifically, the chapter discusses various techniques that you will use, including preprocessing, outlier identification, data consistency, and the normalization or standardization process, used to normalize your data. The chapter further discusses different measurement types and what methods can be used for which types. The chapter also discusses ways to deal with issues you may encounter with inconsistent or dirty data. The chapter takes a more practical approach by integrating several labs with actual game data to demonstrate how you can perform these steps on real game data.","PeriodicalId":137223,"journal":{"name":"Game Data Science","volume":"186 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data Preprocessing\",\"authors\":\"M. S. El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, Anders Drachen\",\"doi\":\"10.1093/oso/9780192897879.003.0002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter focuses on the process of cleaning data and preparing it for further processing. Specifically, the chapter discusses various techniques that you will use, including preprocessing, outlier identification, data consistency, and the normalization or standardization process, used to normalize your data. The chapter further discusses different measurement types and what methods can be used for which types. The chapter also discusses ways to deal with issues you may encounter with inconsistent or dirty data. The chapter takes a more practical approach by integrating several labs with actual game data to demonstrate how you can perform these steps on real game data.\",\"PeriodicalId\":137223,\"journal\":{\"name\":\"Game Data Science\",\"volume\":\"186 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Game Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/oso/9780192897879.003.0002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Game Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/oso/9780192897879.003.0002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This chapter focuses on the process of cleaning data and preparing it for further processing. Specifically, the chapter discusses various techniques that you will use, including preprocessing, outlier identification, data consistency, and the normalization or standardization process, used to normalize your data. The chapter further discusses different measurement types and what methods can be used for which types. The chapter also discusses ways to deal with issues you may encounter with inconsistent or dirty data. The chapter takes a more practical approach by integrating several labs with actual game data to demonstrate how you can perform these steps on real game data.