A Projected Clustering Algorithm and Its Biomedical Application - Clustering Challenges in Biological Network - page 204

Empirical results have shown that IPROCLUS is able to accurately discover
clusters embedded in lower dimensional subspaces. For the synthetic datasets, it
can achieve much higher accuracy than PROCLUS for the scaled datasets while
keeping compatible performance with PROCLUS for the unscaled datasets in all
the three cases. Moreover, IPROCLUS has lower dependence on l than PRO-
CLUS. We also apply our algorithm on the colon tumor dataset, IPROCLUS still
achieves much higher accuracy than PROCLUS.

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