A Preliminary Study on Automatic Breast Cancer Data Classification using Semi-supervised Fuzzy c-Means

Soria et al. have successfully identified six clinically useful and novel subgroups in the Nottingham Tenovus Breast Cancer dataset. However, the methodology used is semi-manual and no single clustering can automatically classify the dataset so far. In this work, two variations of semisupervised Fuzzy c-means (ssFCM) algorithms are explored to classify the Nottingham Tenovus Breast Cancer dataset into the same six subgroups. Three experiments were conducted using the two ssFCM algorithms and the results are evaluated by using inter-rater agreement measures. The ssFCM algorithms identified the six classes of breast cancer but, is in low agreement with Soria’s classification. This, together with high agreement using two clustering algorithms, suggest that the problem lie in the way we use ssFCM rather than in model correctness. Despite this, we consider the ssFCM results promising and note that work for further investigation in ssFCM is required.