Collaborative fuzzy clustering method for large scale interval data
Yan Liu, Fusheng Yu, Jie Ma · 2016
Interval data is often met with in the real world. Fuzzy c-means for interval dataset (IFCM), an effective tool for clustering small or moderate scale interval dataset, can not deal with large scale interval dataset. This paper presents a new clustering method for this problem. The new method introduces collaborative mechanism into the IFCM to raise the efficiency. In other words, this method implements the clustering of large scale interval data by the divide-and-conquer strategy first, and then by collaboratively making use of the clustering results obtained from all the divided moderate scale datasets by IFCM algorithm. Two experiments given in this paper show the effectiveness of the new method. This new method not only gives the same clustering result as IFCM but also costs less time. Therefore, it provides a new approach for dealing with clustering problem of large-scale interval dataset.