FWCMR: A scalable and robust fuzzy weighted clustering based on MapReduce with application to microarray gene expression
作者：
Highlights：
• A fuzzy weighted similarity measurement specially for gene expression clustering.
• A density based soft clustering on the basis of MapReduce.
• Flexible and robust to intrinsic noise with reasonable clustering results.
• Parallelism with least serial bottleneck working with Hadoop, EC2, ….
• A scalable clustering method appropriate for huge volumes of big data.
摘要
•A fuzzy weighted similarity measurement specially for gene expression clustering.•A density based soft clustering on the basis of MapReduce.•Flexible and robust to intrinsic noise with reasonable clustering results.•Parallelism with least serial bottleneck working with Hadoop, EC2, ….•A scalable clustering method appropriate for huge volumes of big data.
论文关键词：MapReduce,Distributed density based clustering,Gene expression microarray,Fuzzy weighted clustering,Decision making,Big data
论文评审过程：Received 28 May 2017, Revised 7 August 2017, Accepted 31 August 2017, Available online 8 September 2017, Version of Record 8 September 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.08.051
