Reliable Agglomerative Clustering
This work addresses clustering reliability for data analysis applications, but it is incremental as it builds on existing agglomerative methods.
The paper tackles the problem of improving agglomerative clustering by extracting all reliable linkages at each step instead of just the smallest one, resulting in adaptive and density-consistent solutions, with experiments on real-world datasets showing performance gains over the standard method.
Standard agglomerative clustering suggests establishing a new reliable linkage at every step. However, in order to provide adaptive, density-consistent and flexible solutions, we study extracting all the reliable linkages at each step, instead of the smallest one. Such a strategy can be applied with all common criteria for agglomerative hierarchical clustering. We also study that this strategy with the single linkage criterion yields a minimum spanning tree algorithm. We perform experiments on several real-world datasets to demonstrate the performance of this strategy compared to the standard alternative.