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What is Compression and Clustering Methods about?

This paper compares two algorithms, Neural+Spectral and PCA+Spectral, for clustering high-dimensional datasets using Spectral Clustering. The algorithms are evaluated on four datasets based on metrics of cluster cohesiveness, separation, and correctness, with Neural+Spectral generally outperforming PCA+Spectral in unsupervised metrics. However, PCA+Spectral shows better performance in the Adjusted Rand Score, indicating it retains more distinguishing information in certain cases.

Author

5dbwcq9sw7

Language

EN