An intelligent and improved density and distance-based clustering approach for industrial survey data classification

作者：

Highlights：

• An intelligent and automatic process to rank the performance in asset management.

• An intelligent system to automatically find the most suitable practice for benchmarking.

• An improved approach to determine the center of clusters.

• Define outlier factors and analysis so that the best and poorest performers can be identified.

摘要

•An intelligent and automatic process to rank the performance in asset management.•An intelligent system to automatically find the most suitable practice for benchmarking.•An improved approach to determine the center of clusters.•Define outlier factors and analysis so that the best and poorest performers can be identified.

论文关键词：Engineering asset management,Clustering,Performance evaluation,Density and distance-based clustering,Outlier analysis,K-means

论文评审过程：Received 21 December 2015, Revised 3 October 2016, Accepted 4 October 2016, Available online 4 October 2016, Version of Record 15 October 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.005