Paper Info
Title
Effective lossless condensed representation and discovery of spatial co-location patterns.
Abstract
A spatial co-location pattern is a set of spatial features frequently co-occuring in nearby geographic spaces. Similar to closed frequent itemset mining, closed co-location pattern (CCP) mining was proposed for losslessly condensing large collections of prevalent co-location patterns. However, the state-of-the-art condensation methods in mining CCP are inspired by closed frequent itemset mining and do not consider the intrinsic characteristics of spatial co-locations, e.g., the participation index and ratio in spatial feature interactions, thus causing serious containment issues in CCP mining. In this paper, we propose a novel lossless condensed representation of prevalent co-location patterns, Super Participation Index-closed (SPI-closed) co-location. An efficient SPI-closed Miner is also proposed to effectively capture the nature of spatial co-location patterns, alongside the development of three additional pruning strategies to make the SPI-closed Miner efficient. This method captures richer feature interactions in spatial co-locations and solves the containment issues in existing CCP methods. A performance evaluation conducted on both synthetic and real-life data sets shows that SPI-closed Miner reduces the number of CCPs by up to 50%, and runs much faster than the baseline CCP mining algorithm described in the literature.
Year
DOI
Venue
2018
10.1016/j.ins.2018.01.011
Information Sciences
Keywords
Field
DocType
Spatial data mining,Spatial co-location patterns,SPI-closed co-location patterns,Lossless condensed representation
Data mining,Data set,Artificial intelligence,Data mining algorithm,Machine learning,Mathematics,Lossless compression
Journal
Volume
ISSN
Citations 
436
0020-0255
10
PageRank 
References 
Authors
0.52
35
4
Name
Order
Citations
PageRank
Lizhen Wang1387.49
Xuguang Bao2394.75
Hongmei Chen3345.17
Longbing Cao42212185.04