A class of two-mode clustering algorithms in a fuzzy setting
Two-mode clustering consists in simultaneously partitioning modes (e.g., objects and variables) of an observed two-mode data matrix. A class of two-mode clustering algorithms in a fuzzy framework is proposed. Starting from the Double k-Means algorithm, different fuzzy proposals are addressed. The first one is the Fuzzy Double k-Means (FDkM) algorithm, providing two fuzzy partitions, one for each mode. A second proposal is the Fuzzy Double k-Means with polynomial fuzzifiers (FDkMpf) algorithm, a general method that includes the FDkM one as a particular case. Finally, a robust extension is introduced and analyzed by using the concept of noise cluster. The adequacy of the proposed algorithms is checked by means of a simulation and two real-case studies.