A comparative analysis of tools for visualizing association rules: A proposal for visualising fuzzy association rules

DOI

10.2991/eusflat-19.2019.72How to use a DOI?

Keywords

Association Rules Fuzzy Association Rules Visualization Frequent itemsets mining Data Mining

Abstract

Discovering new trends and co-occurrences in massive data is a key step when analysing social media, data coming from sensors, etc. Although, nowadays Data Mining is very useful and widely used for the industry, business and government, the main problem of application of machine learning or data mining in other fields is the interpretability and complexity of obtained results for non-expert users in computer or data science. For this reason in the KDD process one of the most important phases is the interpretation and evaluation. In the case of association rules is essential that results are interpretable for experts. One of the most useful tools for this goal is the visualization, because it clarifies the interpretation of results, being easier to understand in order to take a decision or explaining the behaviour of data.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Carlos Fernandez-Basso
AU  - M. Dolores Ruiz
AU  - Miguel Delgado
AU  - Maria J. Martin-Bautista
PY  - 2019/08
DA  - 2019/08
TI  - A comparative analysis of tools for visualizing association rules: A proposal for visualising fuzzy association rules
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 520
EP  - 527
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.72
DO  - 10.2991/eusflat-19.2019.72
ID  - Fernandez-Basso2019/08
ER  -