Effectiveness and Efficiency for Document Clustering in Biomedicine It is crucial for biomedical information retrieval, or clinical decision support in particular, to discover relevant biomedicalclinical information buried in scientific publications. At present, typical search interface is based on keywords as queries and returns a ranked list of documents, which is suited for finding simple factoids but not ideal for more complex information needs required for clinical decision support. A search interface deemed more suitable for this kind of tasks is clusterbased browsing, where retrieved documents are topically grouped for more intuitive exploration. To adopt this model, however, one needs to consider not only the effectiveness but also the efficiency of clustering framework as clustering is a computationally costly operation. As a first step toward a clusterbased browsing information exploration, this paper empirically studies representative feature selectionextraction methods and clustering algorithms for their effectiveness and efficiency.

SEACOIN2.0:an interactive mining and visualization tool for information retrieval, summarization and knowledge discovery The rapidly increasing size of biomedical databases such as Medline requires the use of intelligent data mining methods for information extraction and summarization. Existing biomedical textmining tools have limited capabilities for incorporating citation information during document ranking and for inferring topological and network relationships between biomedical terms. Often too much is returned during summarization leading to information overload. Furthermore, literaturebased discoveries could be hard to interpret if the network is too complex. SEACOIN2.0 can incorporate citation information during document ranking and uses a unique association rule mining algorithm to generate multilevel kary trees. The multilevel trees facilitate efficient information retrieval, visual data exploration, summarization, and hypothesis generation. The system presents graphical summarization via multiple dynamic visualization panels and an interactive word cloud. LexRank algorithm is used to identify salient sentences in top abstracts related to the query. An average Fmeasure of 94% was achieved for document retrieval, and an average precision of 88% was obtained for identification of top cooccurrence terms. SEACOIN2.0 was also used to replicate previously published findings using the literaturebased discovery and EMRbased PheWAS approaches. We present herein SEACOIN2.0 (https:newton.isye.gatech.eduSEACOIN2), an interactive visual mining tool for improved information retrieval, automated multilevel summarization of Medline abstracts, and literaturebased discovery. SEACOIN2.0 addresses the problem of xe2x80x9cinformation overloadxe2x80x9d and allows clinicians and biomedical researchers to meet their information needs.