Open Access

ARTICLE

Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

by Zhihong Ouyang^*, Lei Xue, Feng Ding, Yongsheng Duan

Electronic Countermeasure Institute, National University of Defense Technology, Hefei, 230037, China

* Corresponding Author: Zhihong Ouyang. Email:

Computers, Materials & Continua 2023, 77(1), 983-1008. https://doi.org/10.32604/cmc.2023.042222

Received 23 May 2023; Accepted 24 August 2023; Issue published 31 October 2023

Abstract

Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality. Nevertheless, a common issue with AP clustering is the presence of excessive exemplars, which limits its ability to perform effective aggregation. This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters, without changing the similarity matrix or customizing preference parameters, as done in existing enhanced approaches. An automatic aggregation enhanced affinity propagation (AAEAP) clustering algorithm is proposed, which combines a dependable partitioning clustering approach with AP to achieve this purpose. The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge. Based on these findings, mutually exclusive exemplar detection was conducted on the current AP exemplars, and a pair of unsuitable exemplars for coexistence is recommended. The recommendation is then mapped as a novel constraint, designated mutual exclusion and aggregation. To address this limitation, a modified AP clustering model is derived and the clustering is restarted, which can result in exemplar number reduction, exemplar selection adjustment, and other data point redistribution. The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected. Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison, and many internal and external clustering evaluation indexes are used to measure the clustering performance. The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.

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Keywords

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