Abstract: Incremental clustering algorithm has the ability to solve the problem that large data volume cannot be read into memory at one time. The traditional incremental multiple medoids based fuzzy clustering (IMMFC) algorithm selects only one or a fixed number of center points for each data block, thus leading to a poor clustering performance when the object weights in the cluster are small. A new incremental fuzzy clustering algorithm is proposed for processing large data sets. Firstly, the algorithm divides the large data set into multiple small data blocks and performs fuzzy clustering on each small data block. Then, the target center point is selected from each cluster of each small data block. The number of center points is the minimum number of objects whose sum of weights of objects in the cluster is greater than a threshold. Finally, all selected center points are merged, and the final data block is fuzzy clustered to obtain the final center point. Experimental results show that the algorithm works superior to IMMFC algorithm in the case that the data block accounts for more than 10% of the total data.

Key words: fuzzy clustering, incremental fuzzy clustering, large data set, multiple-center points