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Highlights: [•] Interval attribute weights are proposed and introduced for fuzzy clustering. [•] Genetic mechanism and gradient-based iteration constitute optimization strategy. [•] Data partition and weights can be obtained by minimizing the objective function. [•] Reasonable clustering results can be achieved more easily. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]