摘要翻译:
我们已经使用伽罗瓦格来挖掘水生数据。这些数据是关于水生植物的,即生活在水体中的宏观植物。这些植物具有几种生物学特性,具有几种形态。我们的目的是根据植物的共同性状和形态对其进行聚类,并找出性状之间的关系。Galois格是实现这一目标的有效方法,但适用于二进制数据。在这篇文章中,我们详细介绍了将复杂的水生数据转换成二进制数据的几种方法,并比较了Galois格的初步结果。
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英文标题:
《Mining Complex Hydrobiological Data with Galois Lattices》
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作者:
Aur\'elie Bertaux (CEVH, Lsiit), AGN\`es Braud (LSIIT), Florence Le
Ber (CEVH, Inria Lorraine - Loria)
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最新提交年份:
2008
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Quantitative Biology 数量生物学
二级分类:Quantitative Methods 定量方法
分类描述:All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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英文摘要:
We have used Galois lattices for mining hydrobiological data. These data are about macrophytes, that are macroscopic plants living in water bodies. These plants are characterized by several biological traits, that own several modalities. Our aim is to cluster the plants according to their common traits and modalities and to find out the relations between traits. Galois lattices are efficient methods for such an aim, but apply on binary data. In this article, we detail a few approaches we used to transform complex hydrobiological data into binary data and compare the first results obtained thanks to Galois lattices.
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PDF链接:
https://arxiv.org/pdf/0811.0971