Abstract:Detecting useful patterns from a given data by applying clustering algorithm has many practical applications. In order to perform the task of clustering identifying a set of good exemplars is a challanging job. Success of clustering greatly depends on the initial set of exemplar chosen as representatives. The paper proposes the use of Manhattan distance for identifying high quality exemplars that can act as an initial set of exemplars followed by iteratively refining them on the basis of resemblance between th… Show more
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