Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences

@inproceedings{Budalakoti2005AnomalyDI,
  title={Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences},
  author={Suratna Budalakoti and Ram Akella},
  year={2005}
}
This paper addresses the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. 1 The approach taken uses unsupervised clustering of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by detailed analysis of outliers to detect anomalies. As the LCS measure is expensive to compute, the first part of the paper discusses existing algorithms, such as the Hunt-Szymanski algorithm, that have low time-complexity… CONTINUE READING
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