Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing
This addresses clustering challenges for ordinal data in fields like social sciences or surveys, but it appears incremental as it builds on existing fuzzy clustering techniques.
The paper tackles the problem of clustering ordinal data with overlapping clusters by proposing a method based on sharing membership and likelihood functions, and experiments show its effectiveness with robustness to outliers through value ordering in membership functions.
A task of clustering data given in the ordinal scale under conditions of overlapping clusters has been considered. It's proposed to use an approach based on memberhsip and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.