| 书目名称 | Instance Selection and Construction for Data Mining | | 编辑 | Huan Liu,Hiroshi Motoda | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | The ability to analyze and understand massive data sets lagsfar behind the ability to gather and store the data. To meet thischallenge, knowledge discovery and data mining (KDD) is growingrapidly as an emerging field. However, no matter how powerfulcomputers are now or will be in the future, KDD researchers andpractitioners must consider how to manage ever-growing data which is,ironically, due to the extensive use of computers and ease of datacollection with computers. Many different approaches have been used toaddress the data explosion issue, such as algorithm scale-up and datareduction. Instance, example, or tuple selection pertains to methodsor algorithms that select or search for a representative portion ofdata that can fulfill a KDD task as if the whole data is used.Instance selection is directly related to data reduction and becomesincreasingly important in many KDD applications due to the need forprocessing efficiency and/or storage efficiency. .One of the major means of instance selection is sampling whereby asample is selected for testing and analysis, and randomness is a keyelement in the process. Instance selection also covers methods thatrequire search. Examples can be | | 出版日期 | Book 2001 | | 关键词 | DOM; Time series; algorithms; case-based reasoning; classification; data mining; database; filtering; geneti | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4757-3359-4 | | isbn_softcover | 978-1-4419-4861-8 | | isbn_ebook | 978-1-4757-3359-4Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media Dordrecht 2001 |
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发表于 2025-3-21 16:18:21
