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

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書目名稱Developing Multi-Database Mining Applications影響因子(影響力)




書目名稱Developing Multi-Database Mining Applications影響因子(影響力)學(xué)科排名




書目名稱Developing Multi-Database Mining Applications網(wǎng)絡(luò)公開度




書目名稱Developing Multi-Database Mining Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Developing Multi-Database Mining Applications被引頻次




書目名稱Developing Multi-Database Mining Applications被引頻次學(xué)科排名




書目名稱Developing Multi-Database Mining Applications年度引用




書目名稱Developing Multi-Database Mining Applications年度引用學(xué)科排名




書目名稱Developing Multi-Database Mining Applications讀者反饋




書目名稱Developing Multi-Database Mining Applications讀者反饋學(xué)科排名

夾死提手勢

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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外形

intellect

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

FUSE

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

FUSE

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

CYT

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.

費解

https://doi.org/10.1007/978-1-84996-044-1Clustering; Coding patterns; Exception association rule; Grouping; Heavy association rule; High-frequent

廣口瓶

978-1-4471-2563-1Springer-Verlag London 2010