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  • 标题:Atomic Data Mining Numerical Methods, Source Code SQlite with Python
  • 本地全文:下载
  • 作者:Ali Khwaldeh ; Ali Khwaldeh ; Amani Tahat
  • 期刊名称:Procedia - Social and Behavioral Sciences
  • 印刷版ISSN:1877-0428
  • 出版年度:2013
  • 卷号:73
  • 页码:232-239
  • DOI:10.1016/j.sbspro.2013.02.046
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis paper introduces a recently published Python data mining book (chapters, topics, samples of Python source code written by its authors) to be used in data mining via world wide web and any specific database in several disciplines (economic, physics, education, marketing. etc). The book started with an introduction to data mining by explaining some of the data mining tasks involved classification, dependence modelling, clustering and discovery of association rules. The book addressed that using Python in data mining has been gaining some interest from data miner community due to its open source, general purpose programming and web scripting language; furthermore, it is a cross platform and it can be run on a wide variety of operating systens such as Linux, Windows, FreeBSD, Macintosh, Solaris, OS/2, Amiga, AROS, AS/400, BeOS, OS/390, z/OS, Palm OS, QNX, VMS, Psion, Acorn RISC OS, VxWorks, PlayStation, Sharp Zaurus, Windows CE and even PocketPC. Finally this book can be considered as a teaching textbook for data mining in which several methods such as machine learning and statistics are used to extract high-level knowledge from real-world datasets.
  • 关键词:Python;atomic data;database;data mining algorithms;data model;collaborative intelligence;machine learning
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