Data Analysis and Pattern Recognition in Multiple Databases
Seiten
2016
|
1. Softcover reprint of the original 1st ed. 2014
Springer International Publishing (Verlag)
978-3-319-37727-8 (ISBN)
Springer International Publishing (Verlag)
978-3-319-37727-8 (ISBN)
This book presents an influence analysis between items in time-stamped databases. It covers developments in data analysis and pattern recognition in multiple databases and details the application of intelligent systems modeling to multiple database analysis.
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.
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.
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.
| Erscheinungsdatum | 02.09.2016 |
|---|---|
| Reihe/Serie | Intelligent Systems Reference Library |
| Zusatzinfo | XV, 238 p. 97 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik | |
| Schlagworte | Artificial Intelligence • Computational Intelligence • Data Analysis • Data Mining • data mining and knowledge discovery • Engineering • Expert systems / knowledge-based systems • Intelligent Systems • Multiple Databases • pattern recognition |
| ISBN-10 | 3-319-37727-2 / 3319377272 |
| ISBN-13 | 978-3-319-37727-8 / 9783319377278 |
| Zustand | Neuware |
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