Volume - 5 | Issue - 3 | september 2023
Published
28 October, 2023
The proposed study is on the partial clustering algorithms for cognitive sensor networks that deal with partially observed data. The proposed algorithms aim to estimate clusters in the presence of missing values and leverage data imputation techniques to fill in the gaps in the target and station device matrices. A modified loss function is introduced to shape the cluster centers, and robust Non-negative Matrix Factorization (NMF) algorithms are utilized to enhance the robustness of the clustering process. This research contributes to the field of cognitive sensor networks by providing insights into the challenges of partial clustering and presenting effective algorithms to address them. The proposed methods have the potential to enhance the performance of clustering tasks in various domains, including sensor networks, by accounting for missing data and producing accurate cluster reconstructions.
KeywordsComodule Estimation Partial Clustering Sensor Network Partial Observed Data
