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 analyze 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.
Animesh Adhikari & Jhimli Adhikari
Data Analysis and Pattern Recognition in Multiple Databases [PDF ebook]
Data Analysis and Pattern Recognition in Multiple Databases [PDF ebook]
Cumpărați această carte electronică și primiți încă 1 GRATUIT!
Limba Engleză ● Format PDF ● Pagini 238 ● ISBN 9783319034102 ● Mărime fișier 12.1 MB ● Editura Springer International Publishing ● Oraș Cham ● Țară CH ● Publicat 2013 ● Descărcabil 24 luni ● Valută EUR ● ID 2854845 ● Protecție împotriva copiilor DRM social