Ejecting Outliers to Enhance Robustness of Fuzzy Cluster Ensemble
Li-Jen Kao, Yo‐Ping Huang · 2013
Clustering analysis provides significant contributions to healthcare or medical service. However, relying only on one set of clusters obtained from employing a clustering algorithm, such as fuzzy c-means algorithm (FCM), with an arbitrary initialization may be not robust and accurate in data clustering. The cluster ensemble, the concept of combining multiple clusters produced by a cluster algorithm with several different initializations, can improve the robustness problem. When the outliers were taken into the ensemble may lead the final cluster ensemble to inaccurate results. Thus, outliers should be removed before merging different clusters. In this paper, an adapted FCM algorithm is proposed to detect and remove the outliers. The cluster ensemble framework will employ this adapted FCM algorithm to generate multiple sets of clusters by giving different initialization parameters. Then, a pair wise approach is used to combine those outlier-free clusters. The experimental results verify that the final clusters obtained from the proposed cluster ensemble framework are more robust.