A Data Stream Subspace Clustering Algorithm

• 作者: Liandong Lin Xiandong Xu Xiang Yu
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: DATA mining DATA STREAM SUBSPACE CLUSTERING FEATURE selection DIMENSION reduction
• 年，卷（期）: 2015,（1）
• 页码范围: 97-99
• 页数: 3页
• 分类号: C5

摘要

The main aim of data stream subspace clustering is to find clusters in subspace in rational time accurately. The existing data stream subspace clustering algorithms are greatly influenced by parameters. Due to the flaws of traditional data stream subspace clustering algorithms, we propose SCRP, a new data stream subspace clustering algorithm. SCRP has the advantages of fast clustering and being insensitive to outliers. When data stream changes, the changes will be recorded by the data structure named Region-tree, and the corresponding statistics information will be updated. Further SCRP can regulate clustering results in time when data stream changes. According to the experiments on real datasets and synthetic datasets, SCRP is superior to the existing data stream subspace clustering algorithms on both clustering precision and clustering speed, and it has good scalability to the number of clusters and dimensions.