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Parallel and Distributed Data Mining: An Introduction

  • Mohammed J. Zaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1759)

Abstract

The explosive growth in data collection in business and scientific fields has literally forced upon us the need to analyze and mine useful knowledge from it. Data mining refers to the entire process of extracting useful and novel patterns/models from large datasets. Due to the huge size of data and amount of computation involved in data mining, high-performance computing is an essential component for any successful large-scale data mining application. This chapter presents a survey on large-scale parallel and distributed data mining algorithms and systems, serving as an introduction to the rest of this volume. It also discusses the issues and challenges that must be overcome for designing and implementing successful tools for large-scale data mining.

Keywords

Data Mining Association Rule Frequent Itemsets Association Rule Mining Subspace Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Mohammed J. Zaki
    • 1
  1. 1.Computer Science DepartmentRensselaer Polytechnic InstituteTroy

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