Data Analysis and Pattern Recognition in Multiple Databases

Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Adhikari, Animesh (Autor), Adhikari, Jhimli (Autor), Pedrycz, Witold (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2014.
Edición:1st ed. 2014.
Colección:Intelligent Systems Reference Library, 61
Temas:
Computational intelligence.
Pattern recognition systems.
Data mining.
Computational Intelligence.
Automated Pattern Recognition.
Data Mining and Knowledge Discovery.
Acceso en línea:Texto Completo

MARC

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100 1 |a Adhikari, Animesh.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Data Analysis and Pattern Recognition in Multiple Databases  |h [electronic resource] /  |c by Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz. 
250 |a 1st ed. 2014. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2014. 
300 |a XV, 238 p. 97 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
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490 1 |a Intelligent Systems Reference Library,  |x 1868-4408 ;  |v 61 
505 0 |a From the Contents: Synthesizing Different Extreme Association Rules in Multiple Data Sources -- Clustering items in time-stamped databases induced by stability -- Mining global patterns in multiple large databases -- Clustering Local Frequency Items in Multiple Data Sources -- Mining Patterns of Select Items in Different Data Sources. 
520 |a Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments. 
650 0 |a Computational intelligence. 
650 0 |a Pattern recognition systems. 
650 0 |a Data mining. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Automated Pattern Recognition. 
650 2 4 |a Data Mining and Knowledge Discovery. 
700 1 |a Adhikari, Jhimli.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Pedrycz, Witold.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319034119 
776 0 8 |i Printed edition:  |z 9783319034096 
776 0 8 |i Printed edition:  |z 9783319377278 
830 0 |a Intelligent Systems Reference Library,  |x 1868-4408 ;  |v 61 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-319-03410-2  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712) 

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