Since most real data sets, encountered in cluster analysis are contaminated with background noise or outliers, it is essential to detect and isolate these noise samples from the data set. A few noisy points can affect the clustering procedure by severely biasing the algorithm. An ideal solution to this problem is to identify all the outliers and form a separate noise cluster. In order to do so, we need a technique by which the noise points are automatically identified and removed from the pattern data. This paper presents a modified Adaptive Fuzzy Leader Clustering (AFLC) algorithm that has been used to detect and eliminate the outliers from the data structure and create a separate cluster of the outliers. The AFLC algorithm has an Adaptive Resonance Theory (ART) like architecture with fuzzy learning rules embedded into it.