Content driven clustering algorithm combining density and distance functions
作者：
Highlights：
• A density and distance based hybrid algorithm is presented.
• The algorithm is capable of adapting itself based on the size of the dataset.
• The algorithm can use any evolutionary optimization algorithm.
• Its performance is compared to AP, k-means and DBSCAN by using the V-measure.
• It outperforms all other algorithms while having a low deviation in running times.
摘要
•A density and distance based hybrid algorithm is presented.•The algorithm is capable of adapting itself based on the size of the dataset.•The algorithm can use any evolutionary optimization algorithm.•Its performance is compared to AP, k-means and DBSCAN by using the V-measure.•It outperforms all other algorithms while having a low deviation in running times.
论文关键词：Clustering algorithms,Density based clustering,Distance based clustering,Evolutionary clustering,Window density function
论文评审过程：Received 12 January 2017, Revised 23 August 2018, Accepted 8 October 2018, Available online 12 October 2018, Version of Record 22 October 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.10.007
