基于密度的子空间聚类的聚类评价
Cluster Evaluation of Density Based Subspace Clustering
摘要：
聚类真实世界的数据往往面临着维数灾难，其中真实世界的数据往往包括许多方面。多维数据聚类评价可以通过基于密度的方法来完成。基于DBSCAN聚类引入的范例的密度方法。在这种方法中，将计算具有MinPoints的每个对象邻居的密度。簇的变化将根据每个相邻对象的密度变化而发生。每个对象的邻居通常使用距离函数（例如欧几里得距离）来确定。本文将SUBCLU、FIRES和INSCY方法应用于6 × 1595维合成数据集的聚类。使用IO熵、F1测度、覆盖率、准确率和时间消耗作为性能评估参数。评价结果表明，SUBCLU方法需要相当长的时间来处理子空间聚类，但它的值覆盖率更好。INSCY方法虽然计算时间较长，但精度优于其他两种方法。
英文摘要：
Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically determined using a distance function, for example the Euclidean distance. In this paper SUBCLU, FIRES and INSCY methods will be applied to clustering 6x1595 dimension synthetic datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used as evaluation performance parameters. Evaluation results showed SUBCLU method requires considerable time to process subspace clustering; however, its value coverage is better. Meanwhile INSCY method is better for accuracy comparing with two other methods, although consequence time calculation was longer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
