Abstract
A new version of the k-medians algorithm, the adaptive k-medians algorithm, is introduced to solve clustering problems with the similarity measure defined using the L1-norm. The proposed algorithm first calculates the center of the whole data set as its median. To solve the k-clustering problem (k-1), we formulate the auxiliary clustering problem to generate a set of starting points for the k-th cluster center. Then, the k-medians algorithm is applied starting from the previous (k-1) cluster centers and each point from the set of starting points to solve the k-clustering problem. A solution with the least value of the clustering function is accepted as the solution to the k-clustering problem. We evaluate the performance of the adaptive k-medians algorithm and compare it with other concurrent clustering algorithms using 8 real-world data sets.
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