Pleiades: Subspace Clustering and Evaluation
Pleiades: Subspace Clustering and Evaluation
复制标题
昴宿星团：子空间聚类和评估
DOI：
10.1007/978-3-540-87481-2_44
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发表时间：
2008
影响因子：
1.7
通讯作者：
T. Seidl
中科院分区：
文献类型：
--
作者：
I. Assent;Emmanuel Müller;Ralph Krieger;Timm Jansen;T. Seidl
子空间聚类挖掘存在于属性的局部相关子集中的聚类。在文献中，已经提出了几种方法，沿着不同的质量评估措施。
Pleiades为不同子空间聚类方法的简单比较和评估提供了方法，沿着了几个特定于子空间聚类的质量度量，并可扩展到其他应用领域和算法。它扩展了流行的WEKA挖掘工具，允许将结果与现有算法和数据集进行对比。
Subspace clustering mines the clusters present in locally relevant subsets of the attributes. In the literature, several approaches have been suggested along with different measures for quality assessment.
Pleiadesprovides the means for easy comparison and evaluation of different subspace clustering approaches, along with several quality measures specific for subspace clustering as well as extensibility to further application areas and algorithms. It extends the popular WEKA mining tools, allowing for contrasting results with existing algorithms and data sets.