【摘要】 形式概念分析是一种用于概念聚类的无监督机器学习技术,在数据挖掘、信息检索等很多领域中得到了应用.将概念搜索空间重新组织成一棵前缀树,并构造了一张属性表,利用表中保存的数据对前缀树进行剪枝,使概念聚类的过程仅在一些有效的子空间中执行,进而提出了一种使用属性表的快速概念聚类算法.实验结果表明,该算法在稠密和稀疏的形式背景下均优于NextClosure算法.更多还原

【Abstract】 Formal Concept Analysis is an unsupervised learning technique for conceptual clustering, and has been widely used in many areas such as Data Mining and Information Retrieval. It reorganizes that the search space as a prefix tree (Trie), and employs a new data structure called Attribute Table to prune the Trie. Thus, the procedures of conceptual clustering are localized only in some valid sub-spaces. A fast algorithm for conceptual clustering is presented. Experimental evidence shows that our algorithm performs very well for generating concepts on both dense and sparse contexts.更多还原