Density-Based Centroid Approximation for Initializing Iterative Clustering Algorithms
نویسندگان
چکیده
We present KDI (Kernel Density Initialization), a density-based procedure for approximating centroids for the initialization step of iteration-based clustering algorithms. We show empirically that a rather low number of distance calculations in conjunction with a fast algorithm for nding the highest peaks are suucient for eeectively and eeciently nding a pre-speciied number of good centroids, which can subsequently be used as initial cluster centers. Finally we evaluate our algorithm in several real-world datasets against two well-known methods from the literature and show that KDI achieves favorable results.
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