By Heng Chen, Yi Jin, Yan Zhao, Yongjuan Zhang (auth.), Petra Perner (eds.)
This e-book constitutes the refereed complaints of the thirteenth commercial convention on facts Mining, ICDM 2013, held in manhattan, manhattan, in July 2013. The 22 revised complete papers awarded have been conscientiously reviewed and chosen from 112 submissions. the subjects diversity from theoretical facets of knowledge mining to functions of information mining, reminiscent of in multimedia information, in advertising, finance and telecommunication, in medication and agriculture, and in technique keep an eye on, and society.
Read or Download Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings PDF
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Additional info for Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings
527–538 (2006) 16. : Semi-Supervised Dimensionality Reduction. In: Proceedings of the SIAM International Conference on Data Mining (2007) 17. : A survey of dimension reduction techniques. U. S. Department of Energy, Lawrence Livermore National Laboratory (2002) 18. , pp. 11–15. Pearson Prentice Hall, Madrid (1999) 19. : Data Mining Explained: A Manager’s Guide to CustomerCentric Business Intelligence, ch. 6. Digital press (2001) 20. : Cluster Validity with Fuzzy Sets. Journal of Cybernetics (3), 58–72 (1974) 21.
With time consuming calculations the productivity can be increased by using the AAF in several diﬀerent ways as follows: – The total costs can be reduced due to (a) reduction of license/software costs, (b) reduction of maintenance costs, (c) reduction of ownership costs, (d) reduction of the hardware cost, and (e) reduction of the personnel cost. – The time can be reduced due to (a) provided faster algorithms for the analysis, or (b) to provide parallel executable algorithms. Towards a High Productivity Automatic Analysis Framework 27 Fig.
Clinic for Anesthesia, Innsbruck Medical University Anichstr 35, A-6020 Innsbruck, Austria 4 Research Group Scientiﬁc Computing, Faculty of Computer Science, University of Vienna, Währinger Strasse 29, A-1090 Vienna, Austria Abstract. Due to the recent explosion of research data based on novel scientiﬁc instruments and corresponding experiments, automatic features, in particular in data analysis, has become more essential than ever. In this paper we present a new Automatic Analysis Framework (AAF) that is able to increase the productivity of data analysis.