An Incremental Fuzzy Approach to Finding Event Sequences
Martin, TP & Azvine, B, 2016, An Incremental Fuzzy Approach to Finding Event Sequences. in: Joao Paulo Carvalho, Marie-Jeanne Lesot, Uzay Kaymak, Susana Vieira, Bernadette Bouchon-Meunier, Ronald R Yager (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems: 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part I. Springer International Publishing, pp. 525-536
Recent years have seen increasing volumes of data generated by online systems, such as internet logs, physical access logs, transaction records, email and phone records. These contain multiple overlapping sequences of events related to different individuals and entities. Information that can be mined from these event sequences is an important re-source in understanding current behaviour, predicting future behaviour and identifying non-standard patterns and possible security breaches. Statistical machine learning approaches have had some success but do not allow human insight to be included easily. We have recently presented a framework for representing sequences of related events, with scope for assistance from human experts. This paper describes the framework and
presents a new algorithm which (i) allows the addition of new event sequences as they are identified from data or postulated by a human analyst, and (ii) allows subtraction / removal of sequences that are no longer relevant. Examination of the sequences can be used to further refine and modify general patterns of events.
Full details in the University publications repository