Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

Applying Machine Learning to Software Clustering

Adaptive Affinity Propagation Clustering

Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter ‘preference’ can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clus...

An Improved Extreme Learning Machine for Classification Problem Based on Affinity Propagation Clustering

Xinjie Wu School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China,[email protected] Abstract Extreme learning machine (ELM) is an efficient algorithm for single-hidden layer feedforward neural networks (SLFNs), which can produce good generalization performance in most cases and learn thousands of times faster than conventional p...

Subspace clustering using affinity propagation

This paper proposes a subspace clustering algorithm by introducing attribute weights in the affinity propagation algorithm. A new step is introduced to the affinity propagation process to iteratively update the attribute weights based on the current partition of the data. The relative magnitude of the attribute weights can be used to identify the subspaces in which clusters are embedded. Experi...

Affinity Propagation Clustering Using Path Based Similarity

Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these matrices to choose cluster centers (or exemplars) of respective clusters. H...