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Subject:
From:
Andreas Holzinger <[log in to unmask]>
Reply To:
Andreas Holzinger <[log in to unmask]>
Date:
Fri, 28 Feb 2014 08:34:00 +0100
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SPECIAL SESSION ON ADVANCED METHODS OF INTERACTIVE DATA MINING FOR 
PERSONALIZED MEDICINE

World Intelligence Congress WIC 2014 International Conference on Active 
Media Technology (AMT 2014)
11-14 August 2014 / Warsaw, Poland

http://www.hci4all.at/hci-kdd-amt2014

Paper Submission via the Conference On-Line System LNCS/LNAI Style  due 
to March, 23, 2014 (Camera Ready due to May 11, 2014)

Special Session Organizers: Andreas HOLZINGER, Frank EMMERT-STREIB, 
Matthias DEHMER, Szymon WILK

One of the grand challenges in the life sciences are the large, complex, 
multi-dimensional and weakly structured data sets (big data). These 
increasingly enormous amounts of data require new, efficient and 
user-friendly solutions for data mining and knowledge discovery. The 
trend towards personalized medicine has resulted in an explosive growth 
in the amount of generated (-omics) data from various sources. A 
synergistic combination of methodologies and approaches of two areas 
offer ideal conditions towards solving these challenges: Human-Computer 
Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the 
goal of supporting human intelligence with machine intelligence -- to 
discover new, previously unknown insights into the data. In this special 
session we will focus on three promising research topics:

1) Interactive Graph-based data mining,
2) Interactive Entropy-based data mining, and
3) Interactive Topological data mining.

For example, applying topological techniques to data mining and 
knowledge discovery is a hot and promising future topic. Topological 
methods can be applied to data represented by point clouds, that is, 
finite subsets of the n-dimensional Euclidean space. We can think of the 
input as a sample of some unknown space which one wishes to reconstruct 
and understand. One must distinguish between the ambient (embedding) 
dimension n, and the intrinsic dimension of the data. While n is usually 
high, the intrinsic dimension, being of primary interest, is typically 
small. Therefore, knowing the intrinsic dimensionality of data is the 
first step towards understanding its structure. In addition to the 
expected results gained from basic research, benefits to evidence based 
medicine, treatment and public health can be achieved.

Best regards
Andreas Holzinger, Frank Emmert-Streib, Matthias Dehmer, Szymon Wilk
(Organizers)

-- 
Science is to test ideas -
Engineering is to bring these ideas into Business
-----------------------------------------------------------------------
Assoc.Prof.Dr.Andreas HOLZINGER, PhD, MSc, MPh, BEng, CEng, DipEd, MBCS
Head Research Unit Human-Computer Interaction for Medicine and Health
IBM Watson Think Group, Institute for Medical Informatics (IMI)
Medical University Graz (MUG)
Auenbruggerplatz 2/V, A-8036 Graz (Austria)
Phone: ++43 316 385 13883, Fax: ++43 316 385 13590
http://hci4all.at
http://www.aholzinger.at/
http://genome.tugraz.at/medical_informatics.shtml
Enjoy Thinking. Taming Information. Support Knowledge.
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