posted by organizer: Aholzinger || 1353 views || tracked by 6 users: [display]

HCI-KDD@AMT 2014 : Special Session on Advanced Methods of Interactive Data Mining for Personalized Medicine

FacebookTwitterLinkedInGoogle

Link: http://www.hci4all.at/hci-kdd-amt2014/
 
When Aug 11, 2014 - Aug 11, 2014
Where Warsaw
Submission Deadline Apr 13, 2014
Final Version Due May 11, 2014
Categories    knowledge discovery   interactive data mining   big data   personalized medicine
 

Call For Papers

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.

This special session particularly focuses on building a small but beautiful expert group for joint project proposals

Related Resources

ADAH 2017   Advanced Data Analytics in Health
SDM 2018   SIAM International Conference on Data Mining
EICS 2018   The 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems
PAKDD 2018   The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining
ICDM 2018   18th Industrial Conference on Data Mining ICDM 2018
MLDM 2018   14th International Conference on Machine Learning and Data Mining MLDM 2018
IDEA 2017   KDD 2016 Workshop on Interactive Data Exploration and Analytics (KDD-IDEA)
Emerging trends in ML@ESANN 2018   Special Session - Emerging trends in machine learning: beyond conventional methods and data
IEEE - ICBDA 2018   2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA 2018)--IEEE Xplore and Ei Compendex
ICOMET'17   International Conference on Computational and Mathematical Methods in Engineering and Technology