A Heuristic on Effective and Efficient Clustering on Uncertain Objects

We study the problem of clustering uncertain objects whose locations are uncertain and described by probability density functions. We analyze existing pruning algorithms and experimentally show that there exists a new bottleneck in the performance due to the overhead while pruning candidate clusters for assignment of each uncertain object in each iteration. We further show that by considering squared Euclidean distance, UK-means (without pruning techniques) is reduced to K-means and performs much faster than pruning algorithms, however, with some discrepancies in the clustering results due to the different distance functions used. Thus, we propose Approximate UK-means to heuristically identify objects of boundary cases and re-assign them to better clusters. Our experimental results show that on average the execution time of Approximate UK-means is only 25% more than K-means and our approach reduces the discrepancies of K-means’ clustering results by more than 70% at most.