Clustering algorithms were compared in a study using different objective functions to see how well they group similar data together and separate different groups. The algorithms tested were K-means, Hierarchical, Spectral, Gaussian Mixture Model, and Hidden Markov Model. The researchers found that using multiple objective functions can help find more accurate and distinct clusters efficiently.