Extracting Representative Information on Intra-Organizational Blogging Platforms

At the beginning, each point is treated as an individual cluster. In each step, two clusters with the largest similarity are merged into a new one. The backward strategy is applied after the generation of a new cluster. In the example, in steps (b)–(f), no boundary document is found in the generated cluster and reallocation does not occur. In step (g), two documents located in the middle are merged into one cluster, since they have the largest similarity. If the clustering process continues as the traditional hierarchical clustering method, the nine documents will finally be grouped into two clusters as shown in step (h), which turns out to be inaccurate, because one square document is assigned to a cluster in which most documents are round. In contrast, in REPSET, the square document will be marked as a boundary object (outlined in red), since its average similarity to the whole cluster is less than λ. The backward strategy will then reevaluate the red document and reallocate it if needed. As shown in (i), because the red document’s similarity to the left cluster is than that to the right cluster, it is reallocated to the left cluster.