Compressive K-means
This addresses the scalability problem for K-means clustering in big data applications, offering a more efficient alternative to traditional methods.
The paper tackles the high computational cost of K-means clustering on large datasets by proposing Compressive K-means (CKM), which estimates cluster centers from a compressed sketch, achieving similar performance to Lloyd-Max with a sketch size independent of dataset size and running two orders of magnitude faster on a dataset of 10^7 points.
The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compressive version of K-means (CKM), that estimates cluster centers from a sketch, i.e. from a drastically compressed representation of the training dataset. We demonstrate empirically that CKM performs similarly to Lloyd-Max, for a sketch size proportional to the number of cen-troids times the ambient dimension, and independent of the size of the original dataset. Given the sketch, the computational complexity of CKM is also independent of the size of the dataset. Unlike Lloyd-Max which requires several replicates, we further demonstrate that CKM is almost insensitive to initialization. For a large dataset of 10^7 data points, we show that CKM can run two orders of magnitude faster than five replicates of Lloyd-Max, with similar clustering performance on artificial data. Finally, CKM achieves lower classification errors on handwritten digits classification.