Discovering High Influence Co-location Patterns from Spatial Data Sets

Lei, Lili; Wang, Lizhen; Zeng, Yuming; Zeng, Lanqing

doi:10.1109/icbk.2019.00026

Cited by 4 publications

(1 citation statement)

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“…Fang et al [28] introduced multi-dimensional attributes for spatial instances and applied information entropy technology to construct an influence measure based on the amount of neighbors and the similarity of neighbor pairs' attributes, thus allowing for the discovery of high influence co-locations from instances with attributes. Lei et al [29] used the cosine function to simulate the property of diminishing influence with distance, and they proposed high influence co-locations in which multiple pollution sources affected cancer patients. As cliques are too strict to reflect real world scenes, Ma et al [30] observed that central instances usually had more neighbors than non-central instances in a star-shaped materialized model, so they proposed a new approach to mine sub-prevalent co-locations with dominant features.…”

Section: Spatial Co-location Pattern Miningmentioning

confidence: 99%

High Influencing Pattern Discovery over Time Series Data

Fang

Wang

et al. 2021

IJGI

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A spatial co-location pattern denotes a subset of spatial features whose instances frequently appear nearby. High influence co-location pattern mining is used to find co-location patterns with high influence in specific aspects. Studies of such pattern mining usually rely on spatial distance for measuring nearness between instances, a method that cannot be applied to an influence propagation process concluded from epidemic dispersal scenarios. To discover meaningful patterns by using fruitful results in this field, we extend existing approaches and propose a mining framework. We first defined a new concept of proximity to depict semantic nearness between instances of distinct features, thus applying a star-shaped materialized model to mine influencing patterns. Then, we designed attribute descriptors to perceive attributes of instances and edges from time series data, and we calculated the attribute weights via an analytic hierarchy process, thereby computing the influence between instances and the influence of features in influencing patterns. Next, we constructed influencing metrics and set a threshold to discover high influencing patterns. Since the metrics do not satisfy the downward closure property, we propose two improved algorithms to boost efficiency. Extensive experiments conducted on real and synthetic datasets verified the effectiveness, efficiency, and scalability of our method.

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Section: Spatial Co-location Pattern Miningmentioning

confidence: 99%