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Data Analysis and Pattern Recognition in Multiple Databases

  • Animesh Adhikari
  • Jhimli Adhikari
  • Witold Pedrycz

Part of the Intelligent Systems Reference Library book series (ISRL, volume 61)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 1-19
  3. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 21-42
  4. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 43-60
  5. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 61-74
  6. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 75-108
  7. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 109-129
  8. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 131-155
  9. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 157-181
  10. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 183-208
  11. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 209-229
  12. Animesh Adhikari, Jhimli Adhikari, Witold Pedrycz
    Pages 231-236
  13. Back Matter
    Pages 237-238

About this book

Introduction

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.

Keywords

Data Analysis Intelligent Systems Multiple Databases Pattern Recognition

Authors and affiliations

  • Animesh Adhikari
    • 1
  • Jhimli Adhikari
    • 2
  • Witold Pedrycz
    • 3
  1. 1.Parvatibai Chowgule CollegeMargaoIndia
  2. 2.Narayan Zantye CollegeBicholimIndia
  3. 3.Department of Electrical & Computer EngineeringUniversity of AlbertaEdmontonCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-03410-2
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-03409-6
  • Online ISBN 978-3-319-03410-2
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site