• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.29-36
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• 2009

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, is an important data mining problem with broad applications. Since a sequential pattern in DNA sequences can be a motif, we studied to find sequential patterns in DNA sequences. Most previously proposed mining algorithms follow the exact matching with a sequential pattern definition. They are not able to work in noisy environments and inaccurate data in practice. Theses problems occurs frequently in DNA sequences which is a biological data. We investigated approximate matching method to deal with those cases. Our idea is based on the observation that all occurrences of a frequent pattern can be classified into groups, which we call approximated pattern. The existing PrefixSpan algorithm can successfully find sequential patterns in a long sequence. We improved the PrefixSpan algorithm to find approximate sequential patterns. The experimental results showed that the number of repeats from the proposed method was 5 times more than that of PrefixSpan when the pattern length is 4.