By Topic

QuCOM: K nearest features neighborhood based qualitative spatial co-location patterns mining algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
You Wan ; School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China ; Chenghu Zhou

Spatial Co-location patterns are similar to association rules but explore more relying spatial auto-correlation. They represent subsets of Boolean spatial features whose instances are often located in close geographic proximity. Existing co-location patterns mining researches only concern the spatial attributes, and few of them can handle the huge amount of non-spatial attributes in spatial datasets. Also, they use distance threshold to define spatial neighborhood. However, it is hard to decide the distance threshold for each spatial dataset without specific prior knowledge. Moreover, spatial datasets are not usually even distributed, so a unique distance value cannot fit an irregularly distributed spatial dataset well. Here, we proposed a qualitative spatial co-location pattern, which contains both spatial and non-spatial information. And the k nearest features (k-NF) neighbourhood relation was defined to set the spatial relation between different kinds of spatial features. The k-NF set of one feature's instances was used to evaluate close relationship to the other features. To find qualitative co-location patterns in large spatial datasets, some formal definitions were given, and a QuCOM (Qualitative spatial CO-location patterns Mining) algorithm was proposed. Experimental results on the USA thesis map data prove that QuCOM algorithm is accurate and efficient, and the patterns founded contain more interesting information.

Published in:

Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2011 IEEE International Conference on

Date of Conference:

June 29 2011-July 1 2011