A Refined Rough K-Means Clustering Algorithm based on Minimizing the Effect of Local Outlier Objects to Improve Overlapping Detection

In order to improve the quality of overlapping detection, Rough K-Means (RKM) was proposed as the first kind of rough clustering algorithm. It was found that this recent RKM algorithm known as π RKM is the most powerful and effective version in which there is an increase in the number of objects that are correctly clustered and a decrease in the number objects that are incorrectly clustered compared to the issues which the previous RKM had. However, there are challenges associated with the clustering process which uses RKM as a result of the difficulty in establishing a standard measure for reducing the effect of local outlier objects on a means function. Therefore, the RKM algorithm is refined in this study to address the problem. Through this study we contribute two components. Firstly, we intend to employ the use of Local Outlier Factor (LOF) technique for the discrimination of a number of objects as outliers and secondly, we propose to reduce the effect of local outliers on means function by using a weight. The result of the experiments which were performed through the use of synthetic and real life datasets prove that there is an improvement in the quality of overlapping detection when compared to recent versions.