Clustering and Separating of a Set of Members in Terms of Mutual Distances and Similarities

Abstract. In a case of set members are presented via mutual distances or similarities well-known algorithms for clustering (K-means), grouping (Modulus), and learning (Kozinets’s) are under investigation. Relation-ship between K-means and Modulus algorithms is shown based on idea of unbiased partitioning. The problem of learning to recognize set members (objects or features) is under investigation too. Experimental results are shown for feature recognition (Holzinger’s psychological tests) and for object recognition (small classes of amino-acid sequences) problems