@misc{10481/73037,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10481/73037},
abstract = {The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.},
organization = {COPKIT project from the 8th Programme Framework (H2020) research and innovation programme	786687},
organization = {BIGDATAMED projects	B-TIC-145-UGR18
P18-RT-2947},
publisher = {Elsevier},
keywords = {Big Data},
keywords = {Fuzzy frequent itemset},
keywords = {Fuzzy association rules},
keywords = {Spark},
title = {Spark solutions for discovering fuzzy association rules in Big Data},
doi = {10.1016/j.ijar.2021.07.004},
author = {Fernández Basso, Carlos Jesús and Ruiz Jiménez, María Dolores and Martín Bautista, María José},
}