Quantification and Visualization for Difference of Fuzzy Clustering Results
Mika Sato‐Ilic · 2019
This paper proposes a method to obtain the quantitative difference of fuzzy clustering results and to visualize it in lower dimensional space. Since the result of fuzzy clustering is shown as the degree of belongingness of objects to exploratory obtained clusters, the obtained clusters do not have "explicit order". Therefore, if we obtain multiple results from multiple datasets individually, then we cannot compare the results, since each obtained "order" of clusters for each dataset may differ from each other. However, there is a need to compare the results. For example, if we observe multiple datasets in which each dataset consists of objects and variables and such a dataset is observed for a multiple number of subjects (or times), and if we obtain the clustering result at each subject (or time), then we usually would like to know the difference of the clustering results over the subjects (or times). The proposed method in this paper enables the capturing of the difference based on the mathematical comparability of the obtained clusters over the different datasets corresponding to different subjects (or times) and visualize the difference of clustering results over the subjects (or times). Numerical examples are shown for an improved understanding of the proposed method.