A Minimum Sum-squared Residue for High-order Fuzzy Co-clustering Algorithm

Most existing high-order co-clustering algorithms focus on hard clustering methods，which ignore the problem of overlaps in the clustering structures. In order to analyze the clustering results of data with overlapping clusters more efficiently，we developed a minimum sum-squared residue for high-order fuzzy co-clustering algorithm(MSR-HFCC).The clustering problem is formulated as the problem of minimizing fuzzy sum-squared residue.The update rules for fuzzy memberships were derived，and an iterative algorithm was designed for a co-clustering process.Finally，experimental recults show that the qualities of clustering results of MSR-HFCC are superior to five existing algorithms.