On A Type-2 Fuzzy Clustering Algorithm
Leehter Yao, Kuei-Sung Weng · 2016
Abstract—A Type-2 fuzzy clustering algoritm that integreates Type-2 fuzzy sets with Gustafson-Kessel algorithm is proposed in this paper. The proposed Type-2 Gustafson-Kessel algorithm (T2GKA) is essentially a combination of probabilistic and possibilistic clustering schemes. It will be shown that the T2GKA is less susceptive to noise than the Type-1 GKA. The T2GKA ignores the inlier and outlier interruptions. The clustering results show the robustness of the proposed T2GKA since a reasonable amount of noise data does not affect its clustering performance. A drawback of the conventional GKA is that it can only find clusters of approximately equal volume. To overcome this difficulty, this work uses an algorithm called The Directed Evaluation Ellipsoid Cluster Volume (DEECV) to effectively evaluate the proper ellipsoid volume. The proposed T2GKA is essentially a DEECV based learning algorithm integrated with T2GKA. The experimental results show that the T2GKA can learn suitable sized cluster volume along with a varying dataset structure volume. Keywords-ellipsoids; probabilistic; possibilistic; fuzzy c-means; Gustafson-Kessel algorithm; Type-2 fuzzy clustering I.