Spatial Co-location Pattern Mining Using Delaunay Triangulation

  • G. Kiran Kumar
  • Ilaiah Kavati
  • Koppula Srinivas Rao
  • Ramalingaswamy Cheruku
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

Spatial data mining is the process of finding interesting patterns that may implicitly exist in spatial database. The process of finding the subsets of features that are frequently found together in a same location is called co-location pattern discovery. Earlier methods to find co-location patterns focuses on converting neighbourhood relations to item sets. Once item sets are obtained then can apply any method for finding patterns. The criteria to know the strength of co-location patterns is participation ratio and participation index. In this paper, Delaunay triangulation approach is proposed for mining co-location patterns. Delaunay triangulation represents the closest neighbourhood structure of the features exactly which is a major concern in finding the co-location patterns. The results show that this approach achieves good performance when compared to earlier methodologies.

Keywords

Delaunay triangulation Co-location patterns Participation ratio Participation index Max PI Medoid PI 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • G. Kiran Kumar
    • 1
  • Ilaiah Kavati
    • 2
  • Koppula Srinivas Rao
    • 1
  • Ramalingaswamy Cheruku
    • 3
  1. 1.Department of CSEMLR Institute of TechnologyHyderabadIndia
  2. 2.Department of CSEAnurag Group of InstitutionsHyderabadIndia
  3. 3.Department of Computer Science and EngineeringNational Institute of Technology GoaPondaIndia

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