  As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input
sample to belong to more than one cluster, providing more flexibility than
non-fuzzy clustering methods. However, the accuracy of FCM is subject to false
detections caused by noisy records, weak feature selection and low certainty of
the algorithm in some cases. The false detections are very important in some
decision-making application domains like network security and medical
diagnosis, where weak decisions based on such false detections may lead to
catastrophic outcomes. They are mainly emerged from making decisions about a
subset of records that do not provide enough evidence to make a good decision.
In this paper, we propose a method for detecting such ambiguous records in FCM
by introducing a certainty factor to decrease invalid detections. This approach
enables us to send the detected ambiguous records to another discrimination
method for a deeper investigation, thus increasing the accuracy by lowering the
error rate. Most of the records are still processed quickly and with low error
rate which prevents performance loss compared to similar hybrid methods.
Experimental results of applying the proposed method on several datasets from
different domains show a significant decrease in error rate as well as improved
sensitivity of the algorithm.
