A clustering-based data reduction for very large spatio-temporal datasets

  • Authors:
  • Nhien-An Le-Khac;Martin Bue;Michael Whelan;M-Tahar Kechadi

  • Affiliations:
  • School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;Ecole Polytechnique Universitaire de Lille, Villeneuve d'Ascq cedex, France;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

  • Venue:
  • ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
  • Year:
  • 2010

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Abstract

Today, huge amounts of data are being collected with spatial and temporal components from sources such as meteorological, satellite imagery etc. Efficient visualisation as well as discovery of useful knowledge from these datasets is therefore very challenging and becoming a massive economic need. Data Mining has emerged as the technology to discover hidden knowledge in very large amounts of data. Furthermore, data mining techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. As a consequence, instead of dealing with a large size of raw data, we can use these representatives to visualise or to analyse without losing important information. This paper presents a new approach based on different clustering techniques for data reduction to help analyse very large spatio-temporal data. We also present and discuss preliminary results of this approach.