Zohreh Nazeri

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The goal of data analysis in aviation safety is simple: improve safety. However, the path to this goal is hard to identify. What data mining methods are most applicable to this task? What data are available and how should they be analyzed? How do we focus on the most interesting results? Our answers to these questions are based on a recent research project(More)
This paper discusses a method for incorporating available domain knowledge into data mining techniques in order to improve the interestingness of the discovered rules. Existing domain knowledge is represented by a simple grammar and is used within the algorithms in order to reduce the search space and generate more interesting results. We implemented the(More)
Identifying patterns of factors associated with aircraft accidents is of high interest to the aviation safety community. However, accident data is not large enough to allow a significant discovery of repeating patterns of the factors. We applied the STUCCO 1 algorithm to analyze aircraft accident data in contrast to the aircraft incident data in major(More)
—Air transportation systems are designed to ensure that aircraft accidents are rare events. To minimize these accidents, factors causing or contributing to accidents must be understood and prevented. Previous research has studied accident data to determine these factors. The low rate of accidents however, makes it difficult to discover repeating patterns of(More)
This paper introduces a method for mining co-occurring events from longitudinal data, and applies this method to detecting adverse drug reactions (ADRs) from patient data. Electronic health records are richer than older data sources (such as spontaneous report records) and thus are ideal for ADR mining. However, current data mining methods, such as(More)