Model Based Unsupervised Learning Guided by Abundant Background Samples

Many data sets contain an abundance of background data or samples belonging to classes not currently under consideration. We present a new unsupervised learning method based on fuzzy c-means to learn sub models of a class using background samples to guide cluster split and merge operations. The proposed method demonstrates how background samples can be used to guide and improve the clustering process. The proposed method results in more accurate clusters and helps to escape locally minimum solutions. In addition, the number of clusters is determined for the class under consideration. The method demonstrates remarkable performance on both synthetic 2D and real world data from the MNIST dataset of hand written digits.