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Data Mining and Knowledge Discovery

, Volume 12, Issue 2–3, pp 121–125 | Cite as

Mining Multiple Data Sources: Local Pattern Analysis

  • Shichao Zhang
  • Mohammed J. Zaki
Original Article

Introduction

Many large organizations process data from multiple data sources, such as the different branches of an interstate or international company. Also the Web has emerged as a large, distributed data repository consisting of a variety of data sources and formats. Although the data collected from the Web or multiple local datasets brings us opportunities in improving the quality of decisions, it generates significant challenges at the same time, for example, how to efficiently discover useful knowledge from different data sources and how to integrate them. We call this the multiple data source (MDS) mining problem, and it has recently been recognized as an important research topic in the data mining community.

This problem is difficult to solve due to the fact that MDS mining involves the discovery of useful patterns in multidimensional spaces across diverse sources; and putting all data together from different sources might amass a huge database for centralized processing and...

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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Automatic ControlBeijing University of Aeronautics and AstronauticsBeijingChina
  2. 2.Faculty of Information TechnologyUniversity of Technology SydneyBroadwayAustralia
  3. 3.Computer Science DepartmentRensselaer Polytechnic InstituteTroyUSA

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