Influence function of the error rate of classification based on clustering
نویسندگان
چکیده
Cluster analysis may be performed when one wishes to group similar objects into a given number of clusters. Several algorithms are available in order to construct these clusters. In this talk, focus will be on the generalized k-means algorithm which has the classical k-means procedure as well as the k-medoids algorithm as particular cases. Among the outputs of these clustering techniques, a classification rule is provided in order to classify the objects into one of the clusters. More precisely, let C1(F ), . . . , Ck(F ) denote the clusters constructed under model F by the generalized k-means procedure when the chosen penalty function is Ω. The clustering rule can then be defined as
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