| 书目名称 | Data Mining in Finance | | 副标题 | Advances in Relation | | 编辑 | Boris Kovalerchuk,Evgenii Vityaev | | 视频video | http://file.papertrans.cn/263/262964/262964.mp4 | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | .Data Mining in Finance. presents a comprehensive overviewof major algorithmic approaches to predictive data mining, includingstatistical, neural networks, ruled-based, decision-tree, andfuzzy-logic methods, and then examines the suitability of theseapproaches to financial data mining. The book focuses specifically onrelational data mining (RDM), which is a learning method able to learnmore expressive rules than other symbolic approaches. RDM is thusbetter suited for financial mining, because it is able to make greateruse of underlying domain knowledge. Relational data mining also has abetter ability to explain the discovered rules - an abilitycritical for avoiding spurious patterns which inevitably arise whenthe number of variables examined is very large. The earlier algorithmsfor relational data mining, also known as inductive logic programming(ILP), suffer from a relative computational inefficiency and haverather limited tools for processing numerical data. ..Data Mining in Finance. introduces a new approach, combiningrelational data mining with the analysis of statistical significanceof discovered rules. This reduces the search space and speeds up thealgorithms. The book also p | | 出版日期 | Book 2000 | | 关键词 | Finance; Symbol; algorithms; artificial intelligence; data mining; fuzzy; intelligence; knowledge; knowledge | | 版次 | 1 | | doi | https://doi.org/10.1007/b116453 | | isbn_softcover | 978-1-4757-7332-3 | | isbn_ebook | 978-0-306-47018-9Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 2000 |
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