A Minimum Sum-squared Residue for High-order Fuzzy Co-clustering Algorithm
Huang Shaobi · Wuhan Daxue xuebao. Xinxi kexue ban · 2015
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 results show that the qualities of clustering results of MSR-HFCC are superior to five existing algorithms.