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Titlebook: Knowledge Discovery for Business Information Systems; Witold Abramowicz,Jozef Zurada Book 2002 Springer Science+Business Media New York 20

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書(shū)目名稱(chēng)Knowledge Discovery for Business Information Systems
編輯Witold Abramowicz,Jozef Zurada
視頻videohttp://file.papertrans.cn/544/543863/543863.mp4
叢書(shū)名稱(chēng)The Springer International Series in Engineering and Computer Science
圖書(shū)封面Titlebook: Knowledge Discovery for Business Information Systems;  Witold Abramowicz,Jozef Zurada Book 2002 Springer Science+Business Media New York 20
描述Current database technology and computer hardware allow us togather, store, access, and manipulate massive volumes of raw data inan efficient and inexpensive manner. In addition, the amount of datacollected and warehoused in all industries is growing every year at aphenomenal rate. Nevertheless, our ability to discover critical,non-obvious nuggets of useful information in data that could influenceor help in the decision making process, is still limited. .Knowledge discovery (KDD) and Data Mining (DM) is a new,multidisciplinary field that focuses on the overall process ofinformation discovery from large volumes of data. The field combinesdatabase concepts and theory, machine learning, pattern recognition,statistics, artificial intelligence, uncertainty management, andhigh-performance computing. .To remain competitive, businesses must apply data mining techniquessuch as classification, prediction, and clustering using tools such asneural networks, fuzzy logic, and decision trees to facilitate makingstrategic decisions on a daily basis. ..Knowledge Discovery for Business Information Systems. contains acollection of 16 high quality articles written by experts in the KDDand DM field fro
出版日期Book 2002
關(guān)鍵詞Analysis; Information System; business process; classification; database; hardware; knowledge discovery; li
版次1
doihttps://doi.org/10.1007/b116447
isbn_softcover978-1-4757-7475-7
isbn_ebook978-0-306-46991-6Series ISSN 0893-3405
issn_series 0893-3405
copyrightSpringer Science+Business Media New York 2002
The information of publication is updating

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Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeal networks) can be understood—and the interesting patterns captured in these models appropriately used in a business context—by bringing to bear statistical formalisms and business domain knowledge. The work will be presented in the context of lifetime value modeling for GTE cellular telephone subscribers.
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Hybrid Methodology of Knowledge Discovery for Business Information,blem of visualization of multidimensional data (further called .). In the last part of the text, using a set of business data extracted from a large anonymous database, various machine learning algorithms are used to exemplify hybrid (combined) extraction of useful knowledge.
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Multidimensional Business Process Analysis with the Process Warehouse,im to analyse and improve business processes continuously. This huge historic database, prepared for analysis purposes, enables process analysts to receive comprehensive information on business processes very quickly, at various granularity levels, from various, multidimensional points of view, over a long period of time.
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Book 2002pensive manner. In addition, the amount of datacollected and warehoused in all industries is growing every year at aphenomenal rate. Nevertheless, our ability to discover critical,non-obvious nuggets of useful information in data that could influenceor help in the decision making process, is still l
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Dealing with Dimensions in Data Warehousing,onship of a correct derivation of tables to so-called join trees known from relational theory is shown. The same principles of correctness testing are applicable on specifications of views over dimensions as well as on queries over DWs designed as constellations with explicit dimension hierarchies.
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