摘要翻译：
没有标签的数据分析通常受到无监督机器学习技术的审查。这样的技术提供了更有意义的表示，有助于更好地理解手头的问题，而不是只看数据本身。尽管在许多未标注数据的检查领域存在丰富的专家知识，但这些知识很少被纳入自动分析。专家知识的合并通常是一个将来自不同假设空间的多个数据源组合起来的问题。在这些空格属于不同数据类型的情况下，这项任务变得更加具有挑战性。在这篇论文中，我们提出了一个新的免疫启发的方法，使这种不同类型的数据融合为一个特定的问题集。我们证明了我们的方法在一个假设空间的数据帮助下提供了对另一个假设空间的更好的可视化理解。我们相信我们的模型对探索性数据分析和知识发现领域有一定的启示。
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英文标题：
《STORM - A Novel Information Fusion and Cluster Interpretation Technique》
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作者：
Jan Feyereisl, Uwe Aickelin
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最新提交年份：
2010
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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：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
  Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.
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PDF链接：
https://arxiv.org/pdf/1004.4095