Jun 10


The SequenceMiner was developed to address the problem of detecting and describing anomalies in large data sets from recordings of switch sensors in the cockpits of commercial airliners. SequenceMiner works by performing unsupervised clustering (grouping) of similar sequences together, followed by a detailed analysis of outliers to detect anomalies. In this method, an outlier sequence is defined as a sequence that is far away from a cluster. SequenceMiner also includes new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. This provides analysts with a coherent description of the anomalies identified in the sequence, and why they differ from more “normal” sequences.

The code comes with a document that details any system requirements and explains the algorithm parameters & data format. Example scripts with synthetic data can be found in the ExampleRuns/ directory.

More technical information regarding the algorithm can be found in this paper: S. Budalakoti, A.N. Srivastava, and M.E. Otey. Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety. IEEE Transactions on Systems, Man and cybernetics-Part C: Applications and Reviews, 39:101-113, 2009. Found here


Add New Comment