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sequenceMiner

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sequenceMiner was developed to address the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. 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 by a factor of five. 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.

Software

This software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later.

sequenceMiner NOSA Software Agreement

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