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Knowledge discovery in repeated and very short serial measures with a blocking factor

机译：重复和非常短的串行测量中的知识发现具有阻塞因素

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A new KDD methodology specially designed to analyze repeated very short serial measures called Knowledge Discovery in Serial Measures (KDSM) is introduced. In domains where serial measures are repeated and very short (i.e., very few parameters), traditional methods for series analysis are inappropriate. Moreover, some information is non-serial but is closely connected to serial measures. For this reason, it is necessary to find a suitable way of analyzing these situations. Such an objective is reached with the use of KDSM. The use of KDSM yielded some very interesting results in each of the domains where it was applied (e.g., psychiatry and labor domain). The analysis of these case studies' data with Artificial Intelligence techniques or Statistics in isolation would never have provided such relevant results. While analyzing problems inherent in domains related to the context of serial measures, we found that according to most experts in these domains, it is practically impossible to obtain significant results by using isolated methods or techniques due to the singular structure of these domains. Moreover, if it were possible to obtain some results, they might well be too difficult to be interpreted by the experts. Thus, the main goal of this thesis work is to overcome the limitations of Artificial Intelligence or Statistics techniques. A hybrid methodology which uses the best combination of other more simple techniques, thus facilitating the study of this kind of domains, was chosen.
机译：引入了一种专门设计用于分析重复的非常短的串行度量的新KDD方法，称为“序列度量知识发现（KDSM）”。在重复测量且重复时间很短（即参数很少）的领域中，传统的进行序列分析的方法是不合适的。此外，某些信息不是串行的，但与串行度量紧密相关。因此，有必要找到一种分析这些情况的合适方法。使用KDSM可以达到这样的目的。在应用KDSM的每个领域（例如精神病学和劳动领域），KDSM的使用都产生了一些非常有趣的结果。单独使用人工智能技术或统计数据对这些案例研究的数据进行分析将永远不会提供如此相关的结果。在分析与串行措施相关的领域中固有的问题时，我们发现，根据这些领域的大多数专家的观点，由于这些领域的单一结构，使用隔离的方法或技术实际上不可能获得显着的结果。而且，如果有可能获得一些结果，那么它们可能太难于专家解释了。因此，本文工作的主要目的是克服人工智能或统计技术的局限性。选择了一种混合方法，该方法使用了其他更简单技术的最佳组合，从而促进了此类领域的研究。

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