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首页> 外文期刊>Journal of Infrastructure Systems >Knowledge Discovery in a Facility Condition Assessment Database Using Text Clustering
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Knowledge Discovery in a Facility Condition Assessment Database Using Text Clustering

机译：使用文本聚类的设施条件评估数据库中的知识发现

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Knowledge discovery in databases (KDD) has been applied in many different areas of study including DNA sequence analysis, pattern discovery, document classification, image recognition, and speech recognition. This paper presents the application of KDD in the analysis of a facility condition assessment (FCA) database. The FCA database contains information on facilities located at three campuses within a statewide university system. The case study utilizes cluster analysis for text mining. Cluster analysis is the grouping of objects that are similar within the same cluster and dissimilar to the other clusters. In this analysis, deficiency descriptions from a university's FCA database are the objects being grouped together into clusters. Deficiency descriptions were gathered from 15 housing facilities and 15 academic facilities located at 3 campuses. The results show how some clusters of facility deficiencies are unique with respect to the type of facility and the influence of location on deficiencies of academic facilities. The paper begins with a presentation of background on clustering approaches in KDD. Next, a case study based on a higher education FCA database is presented. Last, the paper concludes by exploring other potential areas of application of the described clustering approach.
机译：数据库中的知识发现（KDD）已应用于许多不同的研究领域，包括DNA序列分析，模式发现，文档分类，图像识别和语音识别。本文介绍了KDD在设施状况评估（FCA）数据库分析中的应用。 FCA数据库包含有关州立大学系统内三个校区设施的信息。该案例研究利用聚类分析进行文本挖掘。聚类分析是在同一聚类中相似但与其他聚类不同的对象的分组。在此分析中，来自大学FCA数据库的缺陷描述是将对象分组在一起的群集。缺陷描述来自3个校园的15个住房设施和15个学术设施。结果表明，在设施类型以及位置对学术设施不足的影响方面，一些设施不足集群是如何独特的。本文首先介绍了KDD中聚类方法的背景。接下来，提出了一个基于高等教育FCA数据库的案例研究。最后，本文通过探索所述聚类方法的其他潜在应用领域作为总结。

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