By Sašo Džeroski, Panče Panov, Dragi Kocev, Ljupčo Todorovski
This booklet constitutes the complaints of the seventeenth overseas convention on Discovery technological know-how, DS 2014, held in Bled, Slovenia, in October 2014. The 30 complete papers integrated during this quantity have been rigorously reviewed and chosen from sixty two submissions. The papers disguise themes comparable to: computational medical discovery; information mining and data discovery; computing device studying and statistical tools; computational creativity; mining medical facts; facts and data visualization; wisdom discovery from clinical literature; mining textual content, unstructured and multimedia information; mining dependent and relational facts; mining temporal and spatial info; mining facts streams; community research; discovery informatics; discovery and experimental workflows; wisdom catch and medical ontologies; information and information integration; good judgment and philosophy of clinical discovery; and purposes of computational equipment in a variety of medical domains.
Read or Download Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings PDF
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Extra info for Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings
Section 6 explains textual visualization, which is utilized as a novel method when combined with both feature selection and the Bayesian Network model. We conclude our study in section 7. 2 Related Work Many different soft computing methods and techniques have been utilized in the area of stock market predictions through the application of specific techniques for selecting important features . Feature selection is one of the Data Mining techniques most commonly used to select a set of relevant features from datasets based on some predetermined criterion .
Music dataset and plots: E, F, G and H are experiments on the Movielens 1M dataset. One can notice that results obtained with the Item-Knn and synthetic data are much lower than using real training data. This behaviour is more pronounced for the Yahoo! Music dataset, which is more sparse than the MovieLens 1M dataset. This conﬁrms the hypothesis that Item-Knn is more sensitive to changes to the noise in local data distribution. 32 N. Antulov-Fantulin et al. ﬁxed-size clickstream sets Ctrain and Ctest .
31] proposed an anonymization framework for string-like data. They used the condensation-based techniques to construct condensed groups and their aggregate statistics. From the aggregate statistics, they calculated the ﬁrst and the second order information statistics of symbol distributions in strings, and generated synthetic, pseudo-string data. But still, many data-privacy researchers agree that high dimensional data poorly resist to de-anonymization  which poses privacy issues for companies, and prevent the usage of real-life datasets for research purposes.