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2018 | OriginalPaper | Chapter

Spatial Co-location Pattern Mining Using Delaunay Triangulation

Authors : G. Kiran Kumar, Ilaiah Kavati, Koppula Srinivas Rao, Ramalingaswamy Cheruku

Published in: Advances in Machine Learning and Data Science

Publisher: Springer Singapore

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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.

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Metadata
Title
Spatial Co-location Pattern Mining Using Delaunay Triangulation
Authors
G. Kiran Kumar
Ilaiah Kavati
Koppula Srinivas Rao
Ramalingaswamy Cheruku
Copyright Year
2018
Publisher
Springer Singapore
Book
Advances in Machine Learning and Data Science

Print ISBN: 978-981-10-8568-0

Electronic ISBN: 978-981-10-8569-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-8569-7_10

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