Abstract
The article describes two new clustering algorithms for DNA nucleotide sequences, summarizes the results of experimental analysis of performance of these algorithms for an ITS-sequence data set, and compares the results with known biologically significant clusters of this data set. It is shown that both algorithms are efficient and can be used in practice.
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Kelarev, A., Kang, B., Steane, D. (2006). Clustering Algorithms for ITS Sequence Data with Alignment Metrics. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_116
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DOI: https://doi.org/10.1007/11941439_116
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
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