[1]JIANG Bing,GU Feiyang,HE Zengyou.Mining Top-k non-redundant distinguishing sequential patterns[J].CAAI Transactions on Intelligent Systems,2018,13(5):680-686.[doi:10.11992/tis.201702019]
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Mining Top-k non-redundant distinguishing sequential patterns
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 5
Page number:
680-686
Column:
学术论文—机器学习
Public date:
2018-09-05
- Title:
- Mining Top-k non-redundant distinguishing sequential patterns
- Keywords:
- distinguishing sequential pattern; breadth-first; redundant sequential patterns; pattern mining; Top-k
- CLC:
- TP393
- DOI:
- 10.11992/tis.201702019
- Abstract:
- Distinct sequential patterns can be used to characterize different categories of datasets. In the field of bioinformatics, logistics management, and e-commerce, the comparison of sequential pattern has a wide range of applications. The goal of the Top-k distinguishing sequential pattern mining is to find k patterns with the highest contrast in a given data set. However, in the Top-k distinguishing sequential pattern mining, there is a redundancy problem with respect to the set of reported sequential patterns, which is not considered by the algorithm. Therefore, in this paper, a non-redundant Top-k distinguishing sequential pattern mining algorithm, breadth-first miner (BFM), is proposed based on breadth-first spanning tree. The redundancy problem is effectively solved using the BFM algorithm. Based on the BFM algorithm, a better algorithm, pruning breadth-first miner (PBFM), is proposed. Through the experimental analysis and comparison on the real data set, the validity of the algorithm is verified.
- References:
-
[1] ZHANG Minghua, KAO Ben, CHEUNG D W, et al. Mining periodic patterns with gap requirement from sequences [J]. ACM transactions on knowledge discovery from data, 2007, 1(2):7.
[2] PEI Jian, WANG Haixun, LIU Jian, et al. Discovering frequent closed partial orders from strings[J]. IEEE transactions on knowledge and data engineering, 2006, 18(11):1467-1481.
[3] YAN Xifeng, HAN Jiawei, AFSHAR R. CloSpan:mining:closed sequential patterns in large datasets[C]//Proceedings of the 3rd SIAM International Conference on Data Mining. San Francisco, USA, 2003:166-177.
[4] JI Xiaonan, BAILEY J, DONG Guozhu. Mining minimal distinguishing subsequence patterns with gap constraints [J]. Knowledge and information systems, 2007, 11(3):259-286.
[5] ZAKI M J. SPADE:an efficient algorithm for mining frequent sequences[J]. Machine learning, 2001, 42(1/2):31-60.
[6] YANG Hao, DUAN Lei, DONG Guozhu, et al. Mining itemset-based distinguishing sequential patterns with gap constraint[C]//Proceedings of the 20th International Conference on Database Systems for Advanced Applications. Hanoi, Vietnam, 2015:39-54.
[7] 杨皓, 段磊, 胡斌, 等. 带间隔约束的Top-k对比序列模式挖掘[J]. 软件学报, 2015, 26(11):2994-3009 YANG Hao, DUAN Lei, HU Bin, et al. Mining Top-k distinguishing sequential patterns with gap constraint[J]. Journal of software, 2015, 26(11):2994-3009
[8] ZHENG Zhigang, WEI Wei, LIU Chunming, et al. An effective contrast sequential pattern mining approach to taxpayer behavior analysis[J]. World wide web, 2016, 19(4):633-651.
[9] GHOSH S, FENG Mengling, NGUYEN H, et al. Risk prediction for acute hypotensive patients by using gap constrained sequential contrast patterns[J]. AMIA annual symposium proceedings, 2014, 2014:1748-1757.
[10] FOURNIER-VIGER P, TSENG VS. Mining Top-K Non-redundant association rules[C]//Proceedings of the 20th International Symposium on Foundations of Intelligent Systems. Macau, China, 2012, 7661:31-40.
[11] FOURNIER-VIGER P, TSENG V S. TNS:mining top-k non-redundant sequential rules[C]//Proceedings of the 28th Symposium on Applied Computing. Coimbra, Portugal, 2013:164-166.
[12] KAMEYA Y, SATO T. RP-growth:Top-k mining of relevant patterns with minimum support raising[C]//Proceedings of the 12th SIAM International Conference on Data Mining. Anaheim, USA, 2012:816-827.
[13] CHAN S, KAO B, YIP C L, et al. Mining emerging substrings[C]//Proceedings of 8th International Conference on Database Systems for Advanced Applications. Kyoto, Japan, 2003:119-126.
[14] JI Xiaonan, BAILEY J, DONG Guozhu. Mining minimal distinguishing subsequence patterns with gap constraints [J]. Knowledge and information systems, 2007, 11(3):259-286.
[15] DENG Kang, ZAIANE O R. An occurrence based approach to mine emerging sequences[C]//Proceedings of the 12th International Conference on Data Warehousing and Knowledge Discovery. Bilbao, Spain, 2010:275-284.
[16] YU H H, CHEN Chunhao, TSENG V S. Mining emerging patterns from time series data with time gap constraint[J]. International journal of innovative computing information and control, 2011, 7(9):5515-5528.
[17] WANG Xianming, DUAN Lei, DONG Guozhu, et al. Efficient mining of density-aware distinguishing sequential patterns with gap constraints[C]//Proceedings of the 19th International Conference on Database Systems for Advanced Applications. Bali, Indonesia, 2014:372-387.
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Last Update:
2018-10-25