@article {17487,
	title = {Visual Exploration across Biomedical Databases},
	journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
	volume = {8},
	year = {2011},
	month = {2011/04//March},
	pages = {536 - 550},
	abstract = {Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration.},
	keywords = {Bioinformatics, Biomedical computing, biomedical databases, cross-database exploration, Data exploration and discovery, Data visualization, database management systems, Databases, Factual, DNA, graph theory, Information Storage and Retrieval, information visualization., Keyword search, medical computing, natural language processing, Proteins, semantic networks, semantics, sequences, text mining, User-Computer Interface, user-defined semantics, visual databases},
	isbn = {1545-5963},
	doi = {10.1109/TCBB.2010.1},
	author = {Lieberman,M.D. and Taheri, S. and Guo,Huimin and Mirrashed,F. and Yahav,I. and Aris,A. and Shneiderman, Ben}
}
@conference {16839,
	title = {NewsStand: a new view on news},
	booktitle = {Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems},
	series = {GIS {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {18:1{\textendash}18:10 - 18:1{\textendash}18:10},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {News articles contain a wealth of implicit geographic content that if exposed to readers improves understanding of today{\textquoteright}s news. However, most articles are not explicitly geotagged with their geographic content, and few news aggregation systems expose this content to users. A new system named NewsStand is presented that collects, analyzes, and displays news stories in a map interface, thus leveraging on their implicit geographic content. NewsStand monitors RSS feeds from thousands of online news sources and retrieves articles within minutes of publication. It then extracts geographic content from articles using a custom-built geotagger, and groups articles into story clusters using a fast online clustering algorithm. By panning and zooming in NewsStand{\textquoteright}s map interface, users can retrieve stories based on both topical significance and geographic region, and see substantially different stories depending on position and zoom level.},
	keywords = {clustering, geotagging, knowledge discovery, text mining},
	isbn = {978-1-60558-323-5},
	doi = {10.1145/1463434.1463458},
	url = {http://doi.acm.org/10.1145/1463434.1463458},
	author = {Teitler,Benjamin E. and Lieberman,Michael D. and Panozzo,Daniele and Sankaranarayanan,Jagan and Samet, Hanan and Sperling,Jon}
}
@conference {15062,
	title = {A topic-based Document Correlation Model},
	booktitle = {Machine Learning and Cybernetics, 2008 International Conference on},
	volume = {5},
	year = {2008},
	month = {2008/07//},
	pages = {2487 - 2491},
	abstract = {Document correlation analysis is now a focus of study in text mining. This paper proposed a Document Correlation Model to capture the correlation between documents from topic level. The model represents the document correlation as the Optimal Matching of a bipartite graph, of which each partition is a document, each node is a topic, and each edge is the similarity between two topics. The topics of each document are retrieved by the Latent Dirichlet Allocation model and Gibbs sampling. Experiments on correlated document search show that the Document Correlation Model outperforms the Vector Space Model on two aspects: 1) it has higher average retrieval precision; 2) it needs less space to store a documentpsilas information.},
	keywords = {bipartite graph optimal matching, data mining, document correlation analysis, document retrieval, Gibbs sampling, Information retrieval, latent Dirichlet allocation model, text analysis, text mining, topic-based document correlation model},
	doi = {10.1109/ICMLC.2008.4620826},
	author = {Jia,Xi-Ping and Peng,Hong and Zheng,Qi-Lun and Zhuolin Jiang and Li,Zhao}
}
@conference {16079,
	title = {Discovering interesting usage patterns in text collections: integrating text mining with visualization},
	booktitle = {Proceedings of the sixteenth ACM conference on Conference on information and knowledge management},
	series = {CIKM {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {213 - 222},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, FeatureLens1, visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current implementation focuses on frequent itemsets of n-grams, as they capture the repetition of exact or similar expressions in the collection. Users can find meaningful co-occurrences of text patterns by visualizing them within and across documents in the collection. This also permits users to identify the temporal evolution of usage such as increasing, decreasing or sudden appearance of text patterns. The interface could be used to explore other text features as well. Initial studies suggest that FeatureLens helped a literary scholar and 8 users generate new hypotheses and interesting insights using 2 text collections.},
	keywords = {digital humanities, frequent closed itemsets, n-grams, text mining, user interface},
	isbn = {978-1-59593-803-9},
	doi = {10.1145/1321440.1321473},
	url = {http://doi.acm.org/10.1145/1321440.1321473},
	author = {Don,Anthony and Zheleva,Elena and Gregory,Machon and Tarkan,Sureyya and Auvil,Loretta and Clement,Tanya and Shneiderman, Ben and Plaisant, Catherine}
}
@conference {16087,
	title = {Exploring erotics in Emily Dickinson{\textquoteright}s correspondence with text mining and visual interfaces},
	booktitle = {Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries},
	series = {JCDL {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {141 - 150},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This paper describes a system to support humanities scholars in their interpretation of literary work. It presents a user interface and web architecture that integrates text mining, a graphical user interface and visualization, while attempting to remain easy to use by non specialists. Users can interactively read and rate documents found in a digital libraries collection, prepare training sets, review results of classification algorithms and explore possible indicators and explanations. Initial evaluation steps suggest that there is a rationale for "provocational" text mining in literary interpretation.},
	keywords = {case studies, humanities, literary criticism, text mining, user interface, Visualization},
	isbn = {1-59593-354-9},
	doi = {10.1145/1141753.1141781},
	url = {http://doi.acm.org/10.1145/1141753.1141781},
	author = {Plaisant, Catherine and Rose,James and Yu,Bei and Auvil,Loretta and Kirschenbaum,Matthew G. and Smith,Martha Nell and Clement,Tanya and Lord,Greg}
}
@conference {14442,
	title = {Is there a grand challenge or X-prize for data mining?},
	booktitle = {Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining},
	series = {KDD {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {954 - 956},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This panel will discuss possible exciting and motivating Grand Challenge problems for Data Mining, focusing on bioinformatics, multimedia mining, link mining, text mining, and web mining.},
	keywords = {Bioinformatics, data mining, grand challenge, image mining, link mining, multimedia mining, text mining, video mining, web mining, X-prize},
	isbn = {1-59593-339-5},
	doi = {10.1145/1150402.1150535},
	url = {http://doi.acm.org/10.1145/1150402.1150535},
	author = {Piatetsky-Shapiro,Gregory and Grossman,Robert and Djeraba,Chabane and Feldman,Ronen and Getoor, Lise and Zaki,Mohammed}
}
