Rank Factor Granules with Fuzzy Collaborative Clustering and Factor Space Theory
Shihu Liu, Fusheng Yu, Patrick S. P. Wang · International Journal of Pattern Recognition and Artificial Intelligence · 2016
This paper makes a discussion on the ranking problem of factor granules where each granule is composed by three parts: the patterns, the factors and the factor-induced information. Hereinto, the factor-induced information refers to the pattern’s attributes and the relationship between any two patterns. The overall ranking process is based on the ideology of fuzzy collaborative clustering, by considering a referential factor granule. The collaborative information, i.e. the partition matrices of factor granules, are used to collaborate the clustering for the referential factor granule. These collaborative information are obtained from different sources by different methods. Specially, one kind is obtained from the qualitative data by factor theory-based method. By comparing the difference of the referential factor granule before and after collaboration in aspect of clustering results, we can sort these factor granules: the little the difference, the closer to the top of the sequence.