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Data Analysis and Pattern Recognition in Multiple Databases: 61 (Intelligent Systems Reference Library, 61) - Tapa dura

Libro 35 de 188: Intelligent Systems Reference Library

Adhikari, Animesh; Adhikari, Jhimli; Pedrycz, Witold

 
9783319034096: Data Analysis and Pattern Recognition in Multiple Databases: 61 (Intelligent Systems Reference Library, 61)
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ISBN 10: 331903409X ISBN 13: 9783319034096
Editorial: Springer, 2013

Sinopsis

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.

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Acerca del autor

Animesh Adhikari is an associate professor in the department of Computer Science, Chowgule College, Goa, India. His education includes: Doctor of Philosophy in Computer Science, Goa University, Goa, India (2009); Master of Technology in Computer Science, Indian Statistical Institute, Kolkata, India (1993); Master of Computer Application, Jadavpur University, Kolkata, India (1991). The dissertations he has written cover: [Ph D] Knowledge Discovery in Databases with an Emphasis on Multiple Large Databases (Goa University, 2009). This dissertation has the following parts: (i) Association analysis and patterns recognition in a database, (ii) Pattern recognition in multiple databases, (iii) Developing better multi-database mining applications; [M Tech] Fractal-based Image Segmentation (Indian Statistical Institute, 1993). Adhikari's areas of interest include: data mining and knowledge discovery, database systems, decision support systems, artificial intelligence, statistics and other related topics. Adhikari's professional activities are: Member, Program Committee, Indian International Conference on Artificial Intelligence (2009) Session Chair, Data Mining and Knowledge Discovery, Indian International Conference on Artificial Intelligence (2009); Reviewer, IEEE Transactions on Knowledge and Data Engineering journal; Member, Editorial Board, International Journal of Knowledge-Based Organizations, IGI Global (2009 - date); Member, Program Committee, Ph D Workshop;International Conference on Management of Data (2009); Reviewer, IEEE Transactions on Parallel and Distibuted Systems journal. Witold Pedrycz is a Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. Dr. Pedrycz is an IEEE Fellow, IFSA Fellow and a Fellow of the Engineering Institute of Canada (EIC). Dr. Pedrycz received the M.Sc., and Ph.D., D.Sci. all from the Silesian University ofTechnology, Gliwice, Poland. His main research interests encompass fundamentals of Computational Intelligence, Granular Computing, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published vigorously in these areas. He is an author of 11 research monographs and over 250 journal papers published in highly reputable journals. His research is highly cited and he is also on the list Highly cited researcher on ISI HighlyCited.com. Dr. Pedrycz is the past president of IFSA and the past president of NAFIPS. He is a recipient of the prestigious Norbert Wiener Award which is one of the two highest awards of the IEEE Systems, Man, and Cybernetics Society. He is also a recipient of the K.S. Fu of NAFIPS and a 2008 IEEE Canada Silver Medal in Computer Engineering Dr. Pedrycz has been a member of numerous program committees of IEEE conferences in the area of Computational Intelligence, Granular Computing, fuzzy sets and neurocomputing. He was a Program Chair of the 2007 Int. Conf on Machine Learning and Cybernetics, August 19-22, 2007, Hong Kong. He was also a General Chair of NAFIPS 2004, June 24-26, 2004, Banff, Alberta- a flagship conference of the NAFIPS Society. Currently Dr. Pedrycz serves as an Associate Editor of IEEE Transactions on Fuzzy Systems. He is on editorial boards of over 10 international journals. Dr Pedrycz is also an Editor-in-Chief of Information Sciences and IEEE Transactions on Systems, Man, and Cybernetics part A.

De la contraportada

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2013
Idioma
Inglés
ISBN 10 
331903409X
ISBN 13 
9783319034096
Encuadernación
Tapa dura
Número de páginas
238
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