Estimating distance threshold for greedy subspace clustering
作者：
Highlights：
• A parameter light subspace clustering method for numerical data is proposed.
• Closeness of objects is based on distance threshold which is estimated from data.
• The method does not require tuning of parameters to output high quality clusters.
• In a single run, the algorithm produces optimal output.
• Empirical results show significant improvement in accuracy and execution time.
摘要
•A parameter light subspace clustering method for numerical data is proposed.•Closeness of objects is based on distance threshold which is estimated from data.•The method does not require tuning of parameters to output high quality clusters.•In a single run, the algorithm produces optimal output.•Empirical results show significant improvement in accuracy and execution time.
论文关键词：Greedy subspace clustering,Parameter estimation,Single linkage clustering
论文评审过程：Received 4 July 2018, Revised 3 April 2019, Accepted 5 June 2019, Available online 6 June 2019, Version of Record 14 June 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.011
