2026-08-26T11:49:01Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/3959 2021-12-13T03:25:19Z 1582963302567:1597824273898 user-ucsy

The Fuzzy k-representatives Algorithm for Clustering Categorical Data Win, Than Htun Hnin, Mya Than This paper presents an algorithm which extends thefuzzy k-means algorithm for clustering categorical objects. Inthis algorithm, use a simple matching dissimilarity measure todeal with categorical objects and uses a frequency-basedmethod to update the representative objects in the clusteringprocess to minimize the clustering cost function. The fuzzy krepresentativealgorithm is that it not only partition objects intoclusters but also shows how confident an object is assigned to acluster. The confidence is determined by the dissimilaritymeasures of an object to all cluster representatives. This paperis provided a method to find the fuzzy cluster representativeswhen the simple matching dissimilarity measure is used forcategorical objects. 2009-08-03 http://hdl.handle.net/20.500.12678/0000003959 https://meral.edu.mm/records/3959