Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper,
a new triclustering approach for data streams is introduced. It follows a streaming scheme
of learning in two steps: offline and online phases. First, the offline phase provides a sum mary model with the components of the triclusters. Then, the second stage is the online
phase to deal with data in streaming. This online phase consists in using the summary
model obtained in the offline stage to update the triclusters as fast as possible with genetic
operators. Results using three types of synthetic datasets and a real-world environmental
sensor dataset are reported. The performance of the proposed triclustering streaming algo rithm is compared to a batch triclustering algorithm, showing an accurate performance
both in terms of quality and running timesMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C
