2018/03/12 by Rayyan Ahmad Khan, Khan, Rayyan Ahmad, Rana Ali Amjad +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Bioinformatics and Genomic Networks #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Social and Information Networks (cs.SI) #cs.AI #cs.CV #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.1803.04459
Submitted to TKDE
openalex publication_date 2018/03/12 · arxiv created 2019/04/15 · arxiv updated 2019/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities. Extended Affinity Propagation is developed by modifying Affinity Propagation such that the desirable features of Affinity Propagation, e.g., exemplars, reasonable computational complexity and no need to specify number of clusters, are preserved while the shortcomings, e.g., the lack of global structure discovery, that limit the applicability of Affinity Propagation are overcome. Extended Affinity Propagation succeeds not only in achieving this goal but can also provide various additional insights into the internal structure of the individual clusters, e.g., refined confidence values, relative cluster densities and local cluster strength in different regions of a cluster, which are valuable for an analyst. We briefly discuss how these insights can help in easily tuning the hyperparameters. We also illustrate these desirable features and the performance of Extended Affinity Propagation on various synthetic and real world datasets.