Knowledge-based clustering - from data to information granules

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A review of clustering techniques and developments (2017, methodology): “The collaborative clustering proposed by Pedrycz [70-73] concerns a process of revealing a structure being common or similar to a number of subsets.”
Concept learning via granular computing: A cognitive viewpoint (2014, background): “It is also worth stressing that information granules permeate almost all human endeavors [2–5, 24, 25, 27 , Note that the aforementioned concepts were learned using constructive methods, which means that the concepts were formed by deﬁning certain concept-forming operators [9,11,16,40,42,48,49].”
Granular Computing: Perspectives and Challenges (2013, background): “Fuzzy sets [61], [65]–[67], [112] provided an important conceptual and algorithmic generalization of sets.”
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, methodology): “Several methods were reported in the literature for estimating the membership functions including the kNN approach [40], the well-known fuzzy c-means method (employed within the FWP) [41], and many other variants [33], [42].”