article · 2009
An Efficient Subsequences Mining Algorithm
Cited by 0Abstract and details only
Abstract
As a step forward to analyzing patterns in sequences, we introduce the problem of mining closed repetitive gapped subsequences and propose efficient solutions. Given a database of sequences where each sequence is an ordered list of events, the pattern we would like to mine is called repetitive gapped subsequence. Different from the sequential pattern mining problem, repetitive support captures not only repetitions of a pattern in different sequences but also the repetitions within a sequence. Given a users-specified support threshold min_sup, we study finding the set of all patterns with repetitive support no less than min_sup. To obtain a compact yet complete result set and improve the efficiency, we also study finding closed patterns. Efficient mining algorithms to find the complete set of desired patterns are proposed based on the idea of instance growth. Our performance study on various datasets shows the efficiency of our approach. A case study is also performed to show the utility of our approach.
Cite this paper
Hong-yan, P. (2009). An Efficient Subsequences Mining Algorithm. https://doi.org/10.1109/icbbe.2009.5162317
Hong-yan, Pan. “An Efficient Subsequences Mining Algorithm.” 2009, pp. 1–4, https://doi.org/10.1109/icbbe.2009.5162317.
Hong-yan, Pan. 2009. “An Efficient Subsequences Mining Algorithm.” https://doi.org/10.1109/icbbe.2009.5162317.
Hong-yan, P. (2009) ‘An Efficient Subsequences Mining Algorithm’. Available at: https://doi.org/10.1109/icbbe.2009.5162317.
@article{hongyan2009efficient,
title = {{An Efficient Subsequences Mining Algorithm}},
author = {Hong-yan, Pan},
pages = {1--4},
year = {2009},
doi = {10.1109/icbbe.2009.5162317}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.