Abstract
AbstractThis paper presents and evaluates a method to combine time-based granulation and three-way decisions to support decision makers in understanding and reasoning on the learned granular structures conceptualising spatio-temporal events. The method uses an existing approach to discover periodic events in the data, such as periods of intense traffic in a city, and provides an original approach to conceptualize such events to support decision makers in: (i) better comprehending the causes that lead to the repetition of such events and/or (ii) increasing the awareness of their effects and consequences. The formal concept analysis is the central tool of the proposed method. This tool is used as a guide in the phase of time-based granulation, which relies on the principle of justified granularity, and as a support for reasoning and making three-way decisions. The main contribution of the paper is an effective and simple method for time-based granulation of events, their observation, and interpretation to support decision making. The method is described with an illustrative example and evaluated on a real data set on forest fires, showing how to define a spatio-temporal DSS model to support decisions in environmental monitoring problems.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Science Applications,Information Systems
Reference39 articles.
1. Bazan JG (2008) Hierarchical classifiers for complex spatio-temporal concepts. In: Peters JF, Skowron A, Rybiński H (eds) Transactions on rough sets IX, Lecture Notes in Computer Science, vol 5390. Springer, Berlin, Heidelberg, pp. 474–750. https://doi.org/10.1007/978-3-540-89876-4_26
2. Burusco A, Fuentes-González R (1998) Construction of the l-fuzzy concept lattice. Fuzzy Sets syst 97(1):109–114
3. Capuano N, Gaeta A, Guarino G, Miranda S, Tomasiello S (2016) Enhancing augmented reality with cognitive and knowledge perspectives: a case study in museum exhibitions. Behav Inform Technol 35(11):968–979
4. Chen J, Yuan W, Cao J, et al. (2019) Traffic-flow prediction via granular computing and stacked autoencoder. Granular Computing 1–11. https://doi.org/10.1007/s41066-019-00167-5
5. Cortez P, Morais A (2007) A data mining approach to predict forest fires using meteorological data. In: MFS Neves J, Machado J (eds) New trends in Artificial Intelligence, volume proceedings of the 13th EPIA 2007, Portuguese Conference on Artificial Intelligence, Guimar˜aes, Portugal, pp 512–523 ISBN:13 978-989-95618-0-9
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