基于用户的交叉关联聚类

User-based Cross-Relational Cluster

聚类是有广泛应用的基本数据挖掘任务．现实生活中大多数的数据是高维的,并且通常相关信息分布在多重关联上．为了保证高效的高维、交叉关联聚类．本文提出了一个有效方法：交叉聚类(CrossClus),该法在用户的指导下执行,既考虑了特征提取的质量,又考虑了聚类的效率．

Clustering is an essential data mining task with numerous applications. Data in most real-life applications are high-dimensional, and the related information often spreads across multiple relations. To ensure effective and efficient high-dimensional, cross-relational clustering, we propose a new approach, called CrossClus, which per-forms cross-relational clustering with user＇s guidance. This method takes care of both quality in feature extraction and efficiency in clustering.