In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Molina-Solana:2017:10.1016/j.asoc.2016.12.044,
author = {Molina-Solana, MJ and Guo, Y and Birch, D},
doi = {10.1016/j.asoc.2016.12.044},
journal = {Applied Soft Computing},
pages = {227--235},
title = {Improving data exploration in graphs with fuzzy logic and large-scale visualisation},
url = {http://dx.doi.org/10.1016/j.asoc.2016.12.044},
volume = {53},
year = {2017}
}
TY - JOUR
AB - This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.
AU - Molina-Solana,MJ
AU - Guo,Y
AU - Birch,D
DO - 10.1016/j.asoc.2016.12.044
EP - 235
PY - 2017///
SN - 1872-9681
SP - 227
TI - Improving data exploration in graphs with fuzzy logic and large-scale visualisation
T2 - Applied Soft Computing
UR - http://dx.doi.org/10.1016/j.asoc.2016.12.044
UR - http://hdl.handle.net/10044/1/43743
VL - 53
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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