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- Related Research Areas
- Data Mining and Knowledge Discovery, Diagnostics
The SequenceMiner was developed to address the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences that arise from recordings of switch sensors in the cockpits of commercial airliners. SequenceMiner works by performing unsupervised clustering (grouping) of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by a detailed analysis of outliers to detect anomalies. SequenceMiner utilizes a new hybrid algorithm for computing the LCS that has been shown to outperform existing algorithms such as Hidden Markov Models. SequenceMiner also includes new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. In this method, an outlier sequence is defined as a sequence that is far away from a cluster. This provides analysts with a coherent description of the anomalies identified in the sequence, and why they differ from more “normal” sequences.
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Popular Resources
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Anomaly Detection and Diagnosis Algorithms for Discrete Symbols
A Publication, Ashok Srivastava's Collection - 10 years, 3 months ago
Shared By: Ashok Srivastava
We present a set of novel algorithms which we call sequenceMiner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise ...
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An Algorithm, Suratna Budalakoti's Collection - 13 years, 6 months ago
Shared By: Suratna Budalakoti
Detecting and describing anomalies in large repositories of discrete symbol sequences. sequenceMiner has been open-sourced! Download the file below to try it out. sequenceMiner was ...
