Random projections fuzzy c-means (RPFCM) for big data clustering


Many contemporary biomedical applications such as physiological monitoring, imaging, and sequencing produce large amounts of data that require new data processing and visualization algorithms. Algorithms such as principal component analysis (PCA), singular value decomposition and random projections (RP) have been proposed for dimensionality reduction. In this paper we propose a new random projection version of the fuzzy c-means (FCM) clustering algorithm denoted as RPFCM that has a different ensemble aggregation strategy than the one previously proposed, denoted as ensemble FCM (EFCM). RPFCM is more suitable than EFCM for big data sets (large number of points, n). We evaluate our method and compare it to EFCM on synthetic and real datasets.




M. Popescu, J. Keller, J. Bezdek, and A. Zare, “Random projections fuzzy c-means (RPFCM) for big data clustering,” in IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE), 2015.
Title = {Random projections fuzzy c-means (RPFCM) for big data clustering},
Author = {Mihail Popescu and James Keller and James Bezdek and Alina Zare},
Booktitle = {IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE)},
Year = {2015},
Month = {Aug.},
Doi = {10.1109/FUZZ-IEEE.2015.7337933},