Abstract
In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining, to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal patterns, high-utility temporal pattern mining is to find each temporal pattern whose utility is greater than or equal to the minimum-utility threshold. To facilitate efficient high-utility temporal pattern mining, several extension and pruning strategies are proposed to reduce the search space. Algorithm HUTPMiner is then proposed to efficiently mine high-utility temporal patterns with the aid of the proposed extension and pruning strategies. Experimental results show that HUTPMiner is able to prune a large number of candidates, thereby achieving high mining efficiency.
- American Sign Language Linguistic Research Project. http://www.bu.edu/aslGoogle Scholar
- Sensor Signal Data Set for Exploring Context Recognition of Mobile Devices. http://www.cis.hut.fi/jhimberg/contextdata/index.shtml.Google Scholar
- R. Agrawal and R. Srikant. 1995. Mining sequential patterns. In Proceedings of the 11th IEEE International Conference on Data Engineering. 3--14.Google Scholar
- C. F. Ahmed, S. K. Tanbeer, and B.-S. Jeong. 2010. A novel approach for mining high-utility sequential patterns in sequence databases. ETRI J. 32, 5 (2010), 676--686.Google ScholarCross Ref
- C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee. 2009. Efficient tree structures for high-utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21, 12 (2009).Google ScholarDigital Library
- O. K. Alkan and P. Karagoz. 2015. CRoM and HuspExt: Improving efficiency of high-utility sequential pattern extraction. IEEE Trans. Knowl. Data Eng. 27, 10 (2015).Google ScholarDigital Library
- J. Allen. 1983. Maintaining knowledge about temporal intervals. Commun. ACM 26, 11 (1983), 832--843.Google ScholarDigital Library
- S. Buffett. 2018. Candidate list maintenance in high-utility sequential pattern mining. In Proceedings of IEEE International Conference on Big Data.Google ScholarCross Ref
- F. Chen, J. Dai, B. Wang, S. Sahu, M. Naphade, and C.-T. Lu. 2011. Activity analysis based on low sample rate smart meters. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 240--248.Google ScholarDigital Library
- Y.-C. Chen, J.-C. Jiang, W.-C. Peng, and S.-Y. Lee. 2010. An efficient algorithm for mining time interval--based patterns in large databases. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 49--58.Google ScholarDigital Library
- Y.-C. Chen, W.-C. Peng, J.-L. Huang, and W.-C. Lee. 2015. Significant correlation pattern mining in smart homes. ACM Trans. Intell. Syst. Technol. 6, 3 (2015).Google ScholarDigital Library
- Y.-C. Chen, W.-C. Peng, and S.-Y. Lee. 2015. Mining temporal patterns in time interval--based data. IEEE Trans. Knowl. Data Eng. 27, 12 (2015).Google ScholarDigital Library
- D.-T. Dinh, B. Le, P. Fournier-Viger, and V.-N. Huynh. 2018. An efficient algorithm for mining periodic high-utility sequential patterns. Appl. Intell. 48, 12 (2018).Google Scholar
- P. Fournier-Viger, C.-W. Wu, S Zida, and V. S. Tseng. 2014. FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning. In Proceedings of the International Symposium on Foundations of Intelligent Systems.Google Scholar
- P. Fournier-Viger, Y. Zhang, J. C.-W. Lin, D.-T. Dinh, and H. B. Le. 2020. Mining correlated high-utility itemsets using various measures. Logic J. IGPL 28, 1 (2020).Google ScholarCross Ref
- P. Fournier-Viger, Y. Zhang, J. C.-W. Lin, H. Fujita, and Y. S. Koh. 2019. Mining local and peak high-utility itemsets. Info. Sci. 481 (2019).Google Scholar
- W. Gan, J. C.-W. Lin, H.-C. Chao, H. Fujita, and P. S. Yu. 2019. Correlated utility-based pattern mining. Info. Sci. 504 (2019).Google Scholar
- W. Gan, J. C.-W. Lin, J. Zhang, H.-C. Chao, H. Fujita, and P. S. Yu. 2020. ProUM: Projection-based utility mining on sequence data. Info. Sci. 513 (2020).Google Scholar
- W. Gan, J. C.-W. Lin, J. Zhang, P. Fournier-Viger, H.-C. Chao, and P. S. Yu. 2019. Fast utility mining on complex sequences data. To appear in IEEE Transactions on Cybernetics.Google Scholar
- J.-W. Huang, B. P. Jaysawal, K.-Y. Chen, and Y.-B. Wu. 2019. Mining frequent and top-K high-utility time interval--based events with duration patterns. Knowl. Info. Syst. (2019).Google Scholar
- H. Kim, M. Marwah, M. Arlitt, G. Lyon, and J. Han. 2011. Unsupervised disaggregation of low frequency power measurements. In Proceedings of the 11th SIAM International Conference on Data Mining. 747--758.Google Scholar
- J. Kolter and M. Johnson. 2011. REDD: A public data set for energy disaggregation research. In Proceedings of International Workshop on Data Mining Applications in Sustainability. 1--6.Google Scholar
- S. Laxman, P. Sastry, and K. Unnikrishnan. 2007. Discovering frequent generalized episodes when events persist for different durations. IEEE Trans. Knowl. Data Eng. 19, 9 (2007), 1188--1201.Google ScholarDigital Library
- Y.-C. Li, J.-S. Yeh, and C.-C. Chang. 2008. Isolated items discarding strategy for discovering high-utility itemsets. Data Knowl. Eng. 64, 1 (2008), 198--217.Google ScholarDigital Library
- J. C.-W. Lin, Y. Li, P. Fournier-Viger, Y. Djenouri, and J. Zhang. 2019. An efficient chain structure to mine high-utility sequential patterns. In Proceedings of IEEE International Conference on Data Mining Workshops.Google Scholar
- J. C.-W. Lin, S. Ren, and P. Fournier-Viger. 2018. MEMU: More efficient algorithm to mine high average-utility patterns with multiple minimum average-utility thresholds. IEEE Access 6 (2018), 7593--7609.Google ScholarCross Ref
- J. C.-W. Lin, S. Ren, P. Fournier-Viger, and T.-P. Hong. 2017. EHAUPM: Efficient high average-utility pattern mining with tighter upper bounds. IEEE Access 5 (2017), 12927--12940.Google ScholarCross Ref
- J. C.-W. Lin, S. Ren, P. Fournier-Viger, T.-P. Hong, and J.-S. Pan. 2018. Efficiently updating the discovered high average-utility itemsets with transaction insertion. Eng. Appl. Artific. Intell. 72 (2018), 136--149.Google ScholarDigital Library
- J. C.-W. Lin, J. M.-T. Wu, P. Fournier-Viger, T.-P. Hong, and T. Li. 2019. Efficient mining of high average-utility sequential patterns from uncertain databases. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics.Google Scholar
- J. C.-W. Lin, J. Zhang, and P. Fournier-Viger. 2017. High-utility sequential pattern mining with multiple minimum utility thresholds. In Proceedings of International Conference on Web and Big Data APWeb-WAIM.Google Scholar
- J. Liu, K. Wang, and B. C. M. Fung. 2012. Direct discovery of high-utility itemsets without candidate generation. In Proceedings of the IEEE International Conference on Data Mining.Google ScholarDigital Library
- J. Liu, K. Wang, and B. C. M. Fung. 2016. Mining high-utility patterns in one phase without generating candidates. IEEE Trans. Knowl. Data Eng. 5 (2016), 1245--1257.Google ScholarDigital Library
- M. Liu and J. Qu. 2012. Mining high-utility itemsets without candidate generation. In Proceedings of the ACM International Conference on Information and Knowledge Management.Google Scholar
- Y. Liu, W. Liao, and A. Choudhary. 2005. A fast high-utility itemsets mining algorithm. In Proceedings of the ACM International Workshop on Utility-based Data Mining.Google Scholar
- F. Moerchen and D. Fradkin. 2010. Robust mining of time intervals with semi-interval partial order patterns. In Proceedings of the SIAM International Conference on Data Mining. 315--326.Google Scholar
- F. Mörchen and A. Ultsch. 2007. Efficient mining of understandable patterns from multivariate interval time series. Data Min. Knowl. Discov. 15, 2 (2007), 181--215.Google ScholarDigital Library
- P. Papapetrou, G. Kollios, S. Sclaroff, and D. Gunopulos. 2005. Discovering frequent arrangements of temporal intervals. In Proceedings of International Conference on Data Mining. 354--361.Google Scholar
- D. Patel, W. Hsu, and M. L. Lee. 2008. Mining relationships among interval-based events for classification. In Proceedings of ACM SIGMOD International Conference on Management of Data. 393--404.Google Scholar
- J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. 2004. Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Trans. Knowl. Data Eng. 16, 11 (2004), 1424--1440.Google ScholarDigital Library
- T. Starner, J. Weaver, and A. Pentland. 1998. Real-time American sign language recognition using desk and wearable computer based video. IEEE Trans. Pattern Anal. Mach. Intell. 20, 12 (1998), 1371--1375.Google ScholarDigital Library
- T. Truong, H. Duong, B. Le, and P. Fournier-Viger. 2019. FMaxCloHUSM: An eficient algorithm for mining frequent closed and maximal high-utility sequences. Eng. Appl. Artific. Intell. 85 (2019), 1--20.Google ScholarCross Ref
- T. Truong-Chi and P. Fournier-Viger. 2004. A survey of high-utility sequential pattern mining. In A Survey of High Utility Sequential Pattern Mining. Springer.Google Scholar
- V. S. Tseng, C.-W. Wu, B.-E. Shie, and P. S. Yu. 2010. Up-Growth: An efficient algorithm for high-utility itemset mining. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Google Scholar
- J.-Z. Wang and J.-L. Huang. 2016. Incremental mining of high-utility sequential patterns in incremental databases. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management.Google ScholarDigital Library
- J.-Z. Wang and J.-L. Huang. 2018. On incremental high-utility sequential pattern mining. ACM Trans. Intell. Syst. Technol. 9, 5 (2018), 55:1--55:26.Google ScholarDigital Library
- J.-Z. Wang, J.-L. Huang, and Y.-C. Chen. 2016. On efficiently mining high-utility sequential patterns. Knowl. Info. Syst. 49, 2 (2016), 597--627.Google ScholarDigital Library
- E. Winarko and J. F. Roddick. 2007. ARMADA-An algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl. Eng. 63, 1 (2007), 76--90.Google ScholarDigital Library
- J. M.-T. Wu, J. C.-W. Lin, M. Pirouz, and P. Fournier-Viger. 2018. TUB-HAUPM: Tighter upper bound for mining high average-utility patterns. IEEE Access 6 (2018), 18655--18669.Google ScholarCross Ref
- J. M.-T. Wu, J. C.-W. Lin, and A. Tamrakar. 2019. High-utility itemset mining with effective pruning strategies. ACM Trans. Knowl. Discov. Data 13, 6 (2019).Google ScholarDigital Library
- S.-Y. Wu and Y.-L. Chen. 2007. Mining nonambiguous temporal patterns for interval-based events. IEEE Trans. Knowl. Data Eng. 19, 6 (2007), 742--758.Google ScholarDigital Library
- S.-Y. Wu and Y.-L. Chen. 2009. Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events. Data Knowl. Eng. 68, 11 (2009), 1309--1330.Google ScholarDigital Library
- T. Xu, T. Li, and X. Dong. 2018. Efficient high-utility negative sequential patterns mining in smart campus. IEEE Access 6 (2018), 23839--23847.Google ScholarCross Ref
- C.-W. Yang, B. P. Jaysawal, and J.-W. Huang. 2017. Subsequence search considering duration and relations of events in time interval--based events sequences. In Proceedings of IEEE International Conference on Data Science and Advanced Analytics.Google ScholarCross Ref
- H. Yao, H. J. Hamilton, and C. J. Butz. 2004. A foundational approach to mining itemset utilities from databases. In Proceedings of the SIAM International Conference on Data Mining.Google Scholar
- J. Yin, Z. Zheng, and L. Cao. 2012. Uspan: An efficient algorithm for mining high-utility sequential patterns. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 660--668.Google Scholar
- J. Yin, Z. Zheng, L. Cao, Y. Song, and W. Wei. 2013. Efficiently mining top-K high-utility sequential patterns. In Proceedings of the IEEE International Conference on Data Mining. 1259--1264.Google Scholar
- M. Zihayat, Y. Chen, and A. An. 2017. Memory-adaptive high-utility sequential pattern mining over data streams. Mach. Learn. 106, 6 (2017).Google Scholar
Index Terms
- Mining High-utility Temporal Patterns on Time Interval–based Data
Recommendations
A survey of incremental high-utility itemset mining
Traditional association rule mining has been widely studied. But it is unsuitable for real-world applications where factors such as unit profits of items and purchase quantities must be considered. High-utility itemset mining HUIM is designed to find ...
Mining Temporal Patterns in Time Interval-Based Data
Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship ...
Mining Top-k Regular High-Utility Itemsets in Transactional Databases
Mining high-utility itemsets is an important task in the area of data mining. It involves exponential mining space and returns a very large number of high-utility itemsets. In a real-time scenario, it is often sufficient to mine a small number of high-...
Comments