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