Our algorithm can determine the number of clusters more accurately with less volatility, and therefore can deduce a better combined clustering result.
clustering ensemble called Spectral Aggregation. This algorithm is based on the 0-1 affinity matrix for clus- tering representation, and then applies ...
clustering ensemble called Spectral Aggregation. This algorithm is based on the 0-1 affinity matrix for clus- tering representation, and then applies ...
Our algorithm can determine the number of clusters more accurately with less volatility, and therefore can deduce a better combined clustering result.
Our algorithm can determine the number of clusters more accurately with less volatility, and therefore can deduce a better combined clustering result.
Aug 10, 2015 · Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data ...
Jan 16, 2018 · A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering ...
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This method integrates spectral clustering and structural sparsity into a joint framework whose ensemble selection result is robust to test instances and ...
Bibliographic details on Spectral aggregation for clustering ensemble.
Apr 26, 2022 · In this paper, we mainly research the process of base cluster aggregation and how to convert hard clustering results into a soft clustering ...