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Titlebook: Developing Multi-Database Mining Applications; Animesh Adhikari,Pralhad Ramachandrarao,Witold Ped Book 2010 Springer-Verlag London 2010 Cl

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書(shū)目名稱(chēng)Developing Multi-Database Mining Applications
編輯Animesh Adhikari,Pralhad Ramachandrarao,Witold Ped
視頻videohttp://file.papertrans.cn/270/269768/269768.mp4
概述One of the first books on multi-database data mining..Discusses the various issues regarding the systematic and efficient development of multi-database mining applications.
叢書(shū)名稱(chēng)Advanced Information and Knowledge Processing
圖書(shū)封面Titlebook: Developing Multi-Database Mining Applications;  Animesh Adhikari,Pralhad Ramachandrarao,Witold Ped Book 2010 Springer-Verlag London 2010 Cl
描述Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate multi-database mining technique is essential to develop better applications. Also, the efficiency of a multi-database mining application could be improved by processing more patterns in the application. A faster algorithm could also play an important role in developing a better application. Thus the efficiency of a multi-database mining application could be enhanced by choosing an appropriate multi-database mining model, an appropriate pattern synthesizing technique, a better pattern representation technique, and an efficient algorithm for solving the problem. This book illustrates each of these issues either in the context of a specific problem, or in general.
出版日期Book 2010
關(guān)鍵詞Clustering; Coding patterns; Exception association rule; Grouping; Heavy association rule; High-frequent
版次1
doihttps://doi.org/10.1007/978-1-84996-044-1
isbn_softcover978-1-4471-2563-1
isbn_ebook978-1-84996-044-1Series ISSN 1610-3947 Series E-ISSN 2197-8441
issn_series 1610-3947
copyrightSpringer-Verlag London 2010
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https://doi.org/10.1007/978-3-658-22761-6l results obtained for both synthetic and real-world datasets and carried out detailed error analysis. Furthermore, we bring a detailed comparative analysis by contrasting the proposed algorithm with some of those reported in the literature. This analysis is completed by taking into consideration th
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1610-3947 hnique, and an efficient algorithm for solving the problem. This book illustrates each of these issues either in the context of a specific problem, or in general.978-1-4471-2563-1978-1-84996-044-1Series ISSN 1610-3947 Series E-ISSN 2197-8441
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An Extended Model of Local Pattern Analysis,l results obtained for both synthetic and real-world datasets and carried out detailed error analysis. Furthermore, we bring a detailed comparative analysis by contrasting the proposed algorithm with some of those reported in the literature. This analysis is completed by taking into consideration th
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Enhancing Quality of Knowledge Synthesized from Multi-database Mining,istics of discovered patterns, like minimum support and minimum confidence. The ACP coding enables more local patterns participate in the knowledge synthesizing/processing activities and thus the quality of synthesized knowledge based on local patterns becomes enhanced significantly with regard to t
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Efficient Clustering of Databases Induced by Local Patterns,asted with the existing clustering algorithms. The efficiency of the clustering process has been improved using several strategies that is by reducing execution time of the clustering algorithm, using more suitable similarity measure, and storing frequent itemsets space efficiently.
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https://doi.org/10.1007/978-1-84996-044-1Clustering; Coding patterns; Exception association rule; Grouping; Heavy association rule; High-frequent
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978-1-4471-2563-1Springer-Verlag London 2010
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