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Advances in data mining. Industrial Conference on Data Mining最新文献

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3. Anwendungsklassen 3. Anwendungsklassen
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-10-12 DOI: 10.1515/9783110676273-003
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引用次数: 0
Vorwort
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-10-12 DOI: 10.1515/9783110676273-202
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引用次数: 0
Stichwortverzeichnis
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-10-12 DOI: 10.1515/9783110676273-013
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引用次数: 0
Frontmatter
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-10-12 DOI: 10.1515/9783110676273-fm
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引用次数: 0
Inhalt 内容
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-10-12 DOI: 10.1515/9783110676273-toc
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引用次数: 0
9. Bewertung 9. 评估
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-10-12 DOI: 10.1515/9783110676273-009
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引用次数: 0
7. Assoziationsanalyse 7. Assoziationsanalyse
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-10-12 DOI: 10.1515/9783110676273-007
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引用次数: 0
Mining Recurring Patterns in Real-Valued Time Series using the Radius Profile 利用半径轮廓挖掘实值时间序列中的重复模式
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2020-01-01 DOI: 10.1109/ICDM50108.2020.00113
Dieter De Paepe, S. Hoecke
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引用次数: 0
LEARNING FROM DATA 从数据中学习
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2019-10-17 DOI: 10.1109/9780470544341.ch4
Doug Fisher, H. Lenz
{"title":"LEARNING FROM DATA","authors":"Doug Fisher, H. Lenz","doi":"10.1109/9780470544341.ch4","DOIUrl":"https://doi.org/10.1109/9780470544341.ch4","url":null,"abstract":"Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice. Further resources are available at www.cambridge.org/9781107607590, including data files for the case studies so students can practice analysing data, and exercises to test students’ understanding.","PeriodicalId":91437,"journal":{"name":"Advances in data mining. Industrial Conference on Data Mining","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86537191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 182
DATA‐MINING CONCEPTS 地理数据挖掘的概念
Advances in data mining. Industrial Conference on Data Mining Pub Date : 2019-10-17 DOI: 10.1002/9781119516057.ch1
Duen Horng
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引用次数: 0
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