【摘要】 高维数据聚类是聚类技术的难点和重点,子空间聚类是实现高维数据集聚类的有效途径。CLIQUE算法是最早提出的基于密度和网格的子空间聚类算法,自动子空间聚类算法的实用性和高效性,带来了子空间聚类算法的空前发展。深入分析CLIQUE算法的优点和局限性;介绍了一些近几年提出的子空间聚类算法,并针对CLIQUE算法的局限性作了改进,聚类的效率和精确性得到了提高;最后对子空间聚类算法的发展趋势进行了讨论。更多还原

【Abstract】 The clustering of high dimensional data is a key problem in clustering methods.Subspace clustering is an effective approach to realize clustering in high dimensional data.As a pioneer density and grid based clustering algorithm,CLIQUE algorithm has,with its practicality and high efficiency,greatly facilitated the development of subspace clustering algorithm.?This paper?analyzes in depth the advantages and limitations of CLIQUE algorithm and introduces several subspace clustering algorithms?put forward in recent years which have all been?updated to?address the limitations of CLIQUE algorithm and therefore improved the efficiency and accuracy for clustering.?In addition,this paper also discusses the development trend of subspace clustering algorithm.更多还原