Hierarchical semi-supervised confidence-based active clustering and its application to the extraction of topic hierarchies from document collections - Nogueira, Bruno Magalhães (2013)
Topic hierarchies are efficient ways of organizing document collections. These structures help users to manage the knowledge contained in textual data. These hierarchies are usually obtained through unsupervised hierarchical clustering algorithms. By not considering the context of the user in the formation of the hierarchical groups, unsupervised topic hierarchies may not attend the user\'s expectations in some cases. One possible solution for this problem is to employ semi-supervised clustering algorithms. These algorithms incorporate the user\'s knowledge through the usage of constraints to the clustering process. However, in the context of semi-supervised hierarchical clustering, the works in the literature do not efficient explore the selection of cases (instances or cluster) to add constraints, neither the interaction of the user with the clustering process. In this sense, in this work we introduce two semi-supervised hierarchical clustering algorithms: HCAC (Hierarchical Confidence-based Active Clustering) and HCAC-LC (Hierarchical Confidence-based Active Clustering with Limited Constraints). These algorithms employ an active learning approach based in the confidence of cluster merges. When a low confidence merge is detected, the user is invited to decide, from a pool of candidate pairs of clusters, the best cluster merge in that point. In this work, we employ HCAC and HCAC-LC in the extraction of topic hierarchies through the SMITH framework, which is also proposed in this thesis. This framework provides a series of well defined activities that allow the user\'s interaction in the generation of topic hierarchies. The active learning approach used in the HCAC-based algorithms, the kind of queries employed in these algorithms, as well as the SMITH framework for the generation of semi-supervised topic hierarchies are innovations to the state of the art proposed in this thesis. Our experimental results indicate that HCAC and HCAC-LC outperform other semi-supervised hierarchical clustering algorithms in diverse scenarios. The results also indicate that semi-supervised topic hierarchies obtained through the SMITH framework are more intuitive and easier to navigate than unsupervised topic hierarchies