SCAF一种有效的子空间聚类算法分类方法
SCAF An effective approach to Classify Subspace Clustering algorithms
摘要：
子空间聚类发现嵌入在高维数据的多个重叠子空间中的聚类。存在许多重要的子空间聚类算法，每个子空间聚类算法都具有不同的特性，这些特性是由使用不同的技术、假设、使用的算法等引起的。一个全面的分类方案是必不可少的，它将考虑所有这些特性来划分各个家庭的子空间聚类方法。属于同一家族的算法将满足共同的特征。这样的分类将有助于未来的开发人员更好地理解要使用的质量标准和类似的算法，以比较结果与他们提出的聚类算法。本文首先提出了子空间聚类算法族SCAF（Subspace Clustering Algorithms Family）的概念。SCAF的特点将基于类，如集群方向，重叠的维度等作为例证，我们进一步提供了一个全面的，系统的描述和比较的几个重要算法属于'轴平行，重叠，密度为基础的' SCAF。
英文摘要：
Subspace clustering discovers the clusters embedded in multiple, overlapping subspaces of high dimensional data. Many significant subspace clustering algorithms exist, each having different characteristics caused by the use of different techniques, assumptions, heuristics used etc. A comprehensive classification scheme is essential which will consider all such characteristics to divide subspace clustering approaches in various families. The algorithms belonging to same family will satisfy common characteristics. Such a categorization will help future developers to better understand the quality criteria to be used and similar algorithms to be used to compare results with their proposed clustering algorithms. In this paper, we first proposed the concept of SCAF (Subspace Clustering Algorithms Family). Characteristics of SCAF will be based on the classes such as cluster orientation, overlap of dimensions etc. As an illustration, we further provided a comprehensive, systematic description and comparison of few significant algorithms belonging to 'Axis parallel, overlapping, density based' SCAF.
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。
