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AntTrend: Stigmergetic Discovery of Spatial Trends

机译：安特伦特：耻辱的空间趋势发现

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摘要

Large amounts of spatially referenced data have been aggregated in various application domains such as Geographic Information Systems (GIS), banking and retailing that motivate the highly demanding field of spatial data mining. So far many beneficial optimization solutions have been introduced inspired by the foraging behavior of ant colonies. In this paper a novel algorithm named AntTrend is proposed for efficient discovery of spatial trends. AntTrend applies the emergent intelligent behavior of ant colonies to handle the huge search space encountered in the discovery of this valuable knowledge. Ant agents in AntTrend share their individual experience of trend detection by exploiting the phenomenon of stigmergy. Many experiments were run on a real banking spatial database to investigate the properties of the algorithm. The results show that AntTrend has much higher efficiency both in performance of the discovery process and in the quality of patterns discovered compared to non-intelligent methods.
机译：在各种应用领域（如地理信息系统（GIS），银行和零售方面都会聚集大量的空间引用数据，这激励了高苛刻的空间数据挖掘领域。到目前为止，通过蚁群的觅食行为引入了许多有益的优化解决方案。在本文中，提出了一种名为Antrend的新算法，以便有效发现空间趋势。安特特先生应用蚁群的紧急智能行为来处理发现这种有价值的知识中遇到的巨大的搜索空间。安特特中的蚂蚁代理通过利用耻辱现象分享了他们个人的趋势检测体验。在真正的银行空间数据库上运行许多实验，以研究算法的性质。结果表明，与非智能方法相比发现的模式的性能以及在发现的模式的质量方面具有更高的效率。

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