Download Discovery Science: 17th International Conference, DS 2014, by Sašo Džeroski, Panče Panov, Dragi Kocev, Ljupčo Todorovski PDF

By Sašo Džeroski, Panče Panov, Dragi Kocev, Ljupčo Todorovski

This publication constitutes the lawsuits of the seventeenth foreign convention on Discovery technology, DS 2014, held in Bled, Slovenia, in October 2014. The 30 complete papers incorporated during this quantity have been conscientiously reviewed and chosen from sixty two submissions. The papers disguise themes resembling: computational clinical discovery; info mining and data discovery; computing device studying and statistical tools; computational creativity; mining medical facts; info and information visualization; wisdom discovery from medical literature; mining textual content, unstructured and multimedia facts; mining based and relational info; mining temporal and spatial information; mining info streams; community research; discovery informatics; discovery and experimental workflows; wisdom trap and clinical ontologies; info and data integration; common sense and philosophy of clinical discovery; and functions of computational tools in a variety of clinical domains.

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Additional resources for Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings

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This paper combines text-mining, feature selection and Bayesian Networks to analyze and extract sentiments from stock-related micro-blogging messages called “StockTwits”. Here, we investigate whether the power of the collective sentiments of StockTwits might be predicted and how these predicted sentiments might help investors and their peers to make profitable investment decisions in the stock market. Specifically, we build Bayesian Networks from terms identified in the tweets that are selected using wrapper feature selection.

The rationale of the model in this paper is that the models are trained from a corpus of manually labelled data to test the computational model, instead of using a sentiment lexicon, such as the SentiWordNet. Existing lexicons are not used in this paper mainly because this research is based on extracting sentiments from financial text, as the decision is to classify text into buy, sell or hold, not merely positive or negative. The vast majority of research papers in the sentiment analysis field focus mainly on domains, including emotional state [9], product review [22] and movie review [14], in which cases SentiWordNet is deemed a suitable lexicon.

Classification of human cancers based on DNA copy number amplification modeling. : Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. : ISCN 2005: An Intl. System for Human Cytogenetic Nomenclature (2005) Recommendations of the Intl. Standing Committee on Human Cytogenetic Nomenclature. : Mixture Modeling of DNA copy number amplification patterns in cancer. , Gra˜ na, M. ) IWANN 2007. LNCS, vol. 4507, pp. 972–979. : Learning Relational Descriptions of Differentially Expressed Gene Groups.

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