2 research outputs found
Association rules over time
Decisions made nowadays by Artificial Intelligence powered systems are
usually hard for users to understand. One of the more important issues faced by
developers is exposed as how to create more explainable Machine Learning
models. In line with this, more explainable techniques need to be developed,
where visual explanation also plays a more important role. This technique could
also be applied successfully for explaining the results of Association Rule
Mining.This Chapter focuses on two issues: (1) How to discover the relevant
association rules, and (2) How to express relations between more attributes
visually. For the solution of the first issue, the proposed method uses
Differential Evolution, while Sankey diagrams are adopted to solve the second
one. This method was applied to a transaction database containing data
generated by an amateur cyclist in past seasons, using a mobile device worn
during the realization of training sessions that is divided into four time
periods. The results of visualization showed that a trend in improving
performance of an athlete can be indicated by changing the attributes appearing
in the selected association rules in different time periods
Crossing Reduction of Sankey Diagram with Barycentre Ordering via Markov Chain
Sankey diagram is popular for analyzing primary flows in network data.
However, the growing complexity of data and hence crossings in the diagram
begin to reduce its readability. In this work, we studied the NP-hard weighted
crossing reduction problem of the Sankey diagram with both the common parallel
form and the circular form. We expect to obtain an ordering of entities that
reduces weighted crossings of links. We proposed a two-staged heuristic method
based on the idea of barycentre ordering and used Markov chain to formulate the
recursive process of obtaining such ordering. In the experiments, our method
achieved 300.89 weighted crossings, compared with the optimum 278.68 from an
integer linear programming method. Also, we obtained much less weighted
crossings (87.855) than the state-of-art heuristic method (146.77). We also
conducted a robust test which provided evidence that our method performed
consistently against the change of complexity in the dataset