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Titlebook: Data Mining and Computational Intelligence; Abraham Kandel,Mark Last,Horst Bunke Book 2001 Physica-Verlag Heidelberg 2001 computational in

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書目名稱Data Mining and Computational Intelligence
編輯Abraham Kandel,Mark Last,Horst Bunke
視頻videohttp://file.papertrans.cn/263/262926/262926.mp4
概述Comprehensive coverage of recent advances in the application of soft computing and fuzzy logic data mining.Also useful as a reference book in data mining, machine learning, fuzzy logic, and artificial
叢書名稱Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Data Mining and Computational Intelligence;  Abraham Kandel,Mark Last,Horst Bunke Book 2001 Physica-Verlag Heidelberg 2001 computational in
描述Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The
出版日期Book 2001
關(guān)鍵詞computational intelligence; data mining; database; database management; fuzzy; fuzzy logic; intelligence; k
版次1
doihttps://doi.org/10.1007/978-3-7908-1825-3
isbn_softcover978-3-7908-2484-1
isbn_ebook978-3-7908-1825-3Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightPhysica-Verlag Heidelberg 2001
The information of publication is updating

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Mining Fuzzy Association Rules in a Database Containing Relational and Transactional Data,echnique for the mining of such rules in databases containing both types of data. This technique, which we call Fuzzy Miner, performs its tasks by the use of fuzzy logic, a set of transformation functions, and by residual analysis. With the transformation functions, new attributes and new item types
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Fuzzy Linguistic Summaries via Association Rules,ata mining. Links between our approach to linguistic summaries and the well-known technique of association rules is shown. The generation of linguistic summaries is implemented by using the authors’ FQUERY for Access package.
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The Fuzzy-ROSA Method: A Statistically Motivated Fuzzy Approach for Data-Based Generation of Small for data-based rule generation has been demonstrated impressively in numerous real-world tasks. However, there are still difficulties in generating small interpretable rule bases efficiently, especially for applications with many input variables. The Fuzzy-ROSA method presented here was developed to
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Mining a Growing Feature Map by Data Skeleton Modelling, knowledge discovery applications. In this article, we present a further extension to the GSOM in which the cluster identification process can be automated. The self-generating ability of the GSOM is used to identify the paths along which the GSOM grew, and these paths are used to develop a skeleton
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Some Practical Applications of Soft Computing and Data Mining,application areas, however, data is more complicated: real-life data is often obtained as an image from a camera rather than a few measurements. Furthermore, this image can also change dynamically. In this paper, we present several examples of how soft computing is related to mining such data.
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