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.

Summarization and Visualization of MultiLevel and MultiDimensional Itemsets Abstract   Frequent itemset (FI) mining aims at discovering relevant patterns from sets of transactions. In this work we focus on multilevel and multidimensional data, which provide a rich description of subjects through multiple features each at different levels of detail. Summarization of FIs has been only marginally addressed so far with specific reference to multilevel and multidimensional FIs. In this paper we fill this gap by proposing SUSHI, a framework for summarizing and visually exploring multilevel and multidimensional FIs. Specifically, SUSHI is based on (i) a similarity function for FIs which takes into account both their extensional (supportbased) and intensional (featurebased) natures; (ii) theoretical results concerning antimonotonicity of support and similarity in multilevel settings, which allow us to propose an efficient clustering algorithm to generate hierarchical summaries; and (iii) two integrated approaches to summary visualization and exploration: a graphbased one, which highlights intercluster relationships, and a treebased one, which emphasizes the relationships between the representative of each cluster and the other FIs in that cluster. SUSHI is evaluated using both a real and a synthetic dataset in terms of effectiveness, efficiency, and understandability of the summary, with reference to three different strategies for choosing cluster representatives. Overall, SUSHI shows to outperform previous approaches and to be a valuable tool to expedite the analysis of FIs. Besides, one of the three strategies for choosing cluster representatives shows to be the most effective one.