Fuzzy Clustering Algorithms in Subjective Classification Tasks

This paper presents a case of study where two of the most used fuzzy clustering algorithms in pattern recognition tasks are analyzed under a classification problem that involves a high degree of subjectivity. The problem consists on the classification of seven types of wood defects called knots. The algorithms are the Abonyi-Szeifert modification of the Gath-Geva algorithm, GGAS, and the Gustafson-Keseel, GK. An improvement to the GK algorithm, GKM, is also proposed and analyzed. Besides the analysis of the algorithms, three different techniques are proposed to generate the design set of samples and the testing set of samples. Results of the study show that the GGAS and the GKM algorithms have a performance close to human performance.