Fuzzy inference based subjective clustering method
T. Miyazaki, Masafumi Hagiwara · 2002
In this paper a new subjective clustering method using fuzzy inference is proposed. Changing some parameters interactively, a user can reflect his/her knowledge or intuition for the clustering. The proposed method takes into account of both: (1) connectivity of data, and (2) linearity of the data distribution. In addition, it represents shapes of clusters by membership functions and uses fuzzy reasoning to reflect the subjectivity of a user effectively. The proposed method is also effective not only for clustering but also for other applications such as data analysis, assumption test, modeling, concept formation support systems, etc. The validity of the proposed method is confirmed by computer simulation.