Towards a granular computing approach based on Formal Concept Analysis for discovering periodicities in data
Towards a granular computing approach based on Formal Concept Analysis for discovering periodicities in data
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DOI：
10.1016/j.knosys.2018.01.032
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
V. Loia;F. Orciuoli;W. Pedrycz
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文献类型：
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作者：
V. Loia;F. Orciuoli;W. Pedrycz
研究与事件的发生和共同发生有关的方面，可以在公共安全和安全等几个领域中实现许多有趣的应用。特别是，在数字取证中，构建嫌疑人的时间轴是有用的，通过分析Facebook和Twitter等社交网络应用程序来重建。解决上述问题的现有数据分析技术的主要局限之一是，它们只能对数据进行单一视图，因此可能会错过有趣的知识的启发。这种限制可以通过考虑更多的视图和应用方法来评估这些视图来克服，允许人类操作员从一个视图移动到更合适的视图。本文着重于时间方面的数据，并提出了一种基于粒度计算的方法来建立多个时间相关的视图，以解释提取的知识，有关的周期性事件的发生。该方法采用形式概念分析（具有时间相关属性）作为实现数据粒度化的算法，并定义了一组粒度计算度量来解释由该算法构造的格中的形式概念，其外延部分由同现事件构成.该方法的适用性证明了提供一个案例研究有关的公共数据集发生在葡萄牙的蒙特西尼奥自然公园的森林火灾。
Studying aspects related to the occurrences and co-occurrences of events enables many interesting applications in several domains like Public Safety and Security. In particular, in Digital Forensics, it is useful to construct the timeline of a suspect, reconstructed by analysing social networking applications like Facebook and Twitter. One of the main limitations of the existing data analysis techniques, addressing the above issues, is their ability to work only on a single view on data and, thus, may miss the elicitation of interesting knowledge. This limitation can be overcome by considering more views and applying methods to asses such views, allowing human operators to move from a view to a more suitable one. This paper focuses on temporal aspects of data and proposes an approach based on Granular Computing to build multiple time-related views in order to interpret the extracted knowledge concerning the periodic occurrences of events. The proposed approach adopts Formal Concept Analysis (with time-related attributes) as an algorithm to realize granulations of data and defines a set of Granular Computing measures to interpret the formal concepts, whose extensional parts are formed by co-occurred events, in the lattices constructed by such algorithm. The applicability of the approach is demonstrated by providing a case study concerning a public dataset on forest fires occurred in the Montesinho natural park in Portugal.