AntTrend: Stigmergetic Discovery of Spatial Trends

  • Ashkan Zarnani
  • Masoud Rahgozar
  • Caro Lucas
  • Azizollah Memariani
Conference paper

DOI: 10.1007/11875604_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)
Cite this paper as:
Zarnani A., Rahgozar M., Lucas C., Memariani A. (2006) AntTrend: Stigmergetic Discovery of Spatial Trends. In: Esposito F., Raś Z.W., Malerba D., Semeraro G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science, vol 4203. Springer, Berlin, Heidelberg

Abstract

Large amounts of spatially referenced data have been aggregated in various application domains such as Geographic Information Systems (GIS), banking and retailing that motivate the highly demanding field of spatial data mining. So far many beneficial optimization solutions have been introduced inspired by the foraging behavior of ant colonies. In this paper a novel algorithm named AntTrend is proposed for efficient discovery of spatial trends. AntTrend applies the emergent intelligent behavior of ant colonies to handle the huge search space encountered in the discovery of this valuable knowledge. Ant agents in AntTrend share their individual experience of trend detection by exploiting the phenomenon of stigmergy. Many experiments were run on a real banking spatial database to investigate the properties of the algorithm. The results show that AntTrend has much higher efficiency both in performance of the discovery process and in the quality of patterns discovered compared to non-intelligent methods.

Keywords

Spatial Data Mining Ant Colony Optimization Spatial Trends 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ashkan Zarnani
    • 1
  • Masoud Rahgozar
    • 1
  • Caro Lucas
    • 1
  • Azizollah Memariani
    • 2
  1. 1.Database Research Group (CIPCE), Electrical and Computer Engineering DepartmentUniversity of TehranTehranIran
  2. 2.Department of Industrial EngineeringBu-Ali Sina UniversityHamedanIran

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