Bisecting K-Means algorithm starts with a single cluster of all the documents and works by applying K-Means to split this cluster into two sub-clusters (bisecting step).
As mentioned before, Bisecting K-Means terminates when a stopping criterion is met.
In the exhaustive version of the algorithm used in this paper, the process of selecting and bisecting leaf clusters continues until leaf clusters are singletons (i.
The run time of Bisecting Incremental K-Means method is O([Nlog.
The algorithm involves time consuming processes such as (a) Bisecting of a leaf cluster and (b) Computing the BIC values of a leaf cluster and of its descendant nodes which takes time linear to the number of documents in a cluster.
In the following, we compare BIC-Means (the proposed method) with Incremental Bisecting K-Means (the most successful variant of K-Means), the benchmark method in all our experiments.
The performance of BIC-Means: BIC-Means performs at least as well as Incremental Bisecting K-Means (producing exactly the same clustering results, only faster).
The performance of Dynamic BIC-Means: Dynamic BIC-Means approaches the clustering solutions of the standard K-Means approaches such as the Incremental Bisecting K-Means.
This first set of experiment focuses on the evaluation of the clustering quality of BIC-Means compared with Bisecting Incremental K-means.
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Outside In reads like an infrared horizon; the nebulous halo of Iota suggests the galactic--Houshiary's white canvases remain abstract, achieving an immersive immateriality that in certain cases produces cognitive destabilizations that border on the mystical.