C04-1069 clusters . Here we use a simple K-Means approach to cluster document
C02-1144 cluster constructed by Bisecting K-means is obviously of inferior quality
C02-1081 group . To establish classes the k-means algorithm was em - ployed . This
C02-1081 based on 2 to 4 factors . The k-means clustering algorithm with a fixed
C02-1144 similar . This phase resembles K-means in that every element is assigned
C02-1144 algorithm , we applied the basic K-means algorithm twice ( a = 2 in Section
C02-1144 for Buckshot . For the Bisecting K-means algorithm , we applied the basic
C02-1144 as the initial K centroids of K-means clustering . The sample size
C02-1144 one experiment , CBC outperforms K-means by 4.25 % . By comparing the
C02-1144 selecting initial centroids in K-means by combining it with average-link
C04-1068 describing how clusters created by K-Means are matched to newsgroups ( Section
C04-1069 approaches to cluster document set . K-Means and hierarchical clustering are
C02-1144 features per word than S . For the K-means and Buckshot algorithms , we
C04-1069 document set r ; secondly , we use K-Means approach to cluster these 10
C04-1068 representation used to form the input to K-Means . 3.1 Data Representation As
C02-1144 exactly to WordNets cell class . K-means and Buckshot produced fairly
C02-1144 its closest centroid . Unlike K-means , the number of clusters is not
C02-1144 complexity . Table 2 shows that K-means , Buckshot and Average-link have
C02-1144 consists of applying the basic K-means algorithm a times with K = 2
C02-1144 of elements to be clustered . K-means is a family of partitional clustering
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