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ADMA
2009
Springer

Mining Compressed Repetitive Gapped Sequential Patterns Efficiently

12 years 3 days ago
Mining Compressed Repetitive Gapped Sequential Patterns Efficiently
Mining frequent sequential patterns from sequence databases has been a central research topic in data mining and various efficient mining sequential patterns algorithms have been proposed and studied. Recently, a novel sequential pattern mining research, called mining repetitive gapped subsequences, has attracted the attention of many researchers. However, the number of repetitive gapped subsequences generated by even these closed mining algorithms may be too large to understand for users, especially when support threshold is low. In this paper, we propose the problem of how to compress repetitive gapped sequential patterns. A novel distance measure of repetitive gapped sequential patterns and an efficient representative pattern checking scheme, δ -dominate sequential pattern checking are proposed. We also develop an efficient algorithm, CRGSgrow (Compressing Repetitive Gapped Sequential pattern grow), including an efficient pruning strategy, SyncScan. An empirical study with both rea...
Yongxin Tong, Zhao Li, Dan Yu, Shilong Ma, Zhiyuan
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ADMA
Authors Yongxin Tong, Zhao Li, Dan Yu, Shilong Ma, Zhiyuan Cheng, Ke Xu
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