ABSTRACT
Sequential pattern mining is a technique which finds out frequent patterns from the data set with a time attribute. Dynamic weighted sequential pattern mining can be applied to a computing environment that is constantly changed according to time period and it can be applied to a variety of environments with changes of weight value. In this paper, we propose a new sequential pattern mining method to discover frequent sequential patterns by applying the dynamic weight. This method reduces the number of candidate patterns by dynamic weight according to the relative time sequence. This also decreases the memory usage and processing time compared with existing methods. And we show the importance of processing USN (Ubiquitous Sensor Network) stream data with hash map structure and the method of renewing current frequent pattern list.
- R. Agrawal, R. Srikant. Mining sequential patterns. In Proceedings of the 11th International Conference on Data Engineering ICDE'95 (Apr. 1995). 3--14. Google ScholarDigital Library
- J. Pei, J. Han, B. M. Asl, H. Pinto, Q. chen U. Dayal, M. Hus. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of International Conference on Data engineering. ICDE'01. 215--226. 2001. Google ScholarDigital Library
- U. Yun, J.J. Leggett. WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In Proceedings of the 4th SIAM International Conference on Data Mining. SDM '05. USA. 636--640.Google Scholar
- C.F. Ahmed, S.K. Tanbeer, B.-S. Jeong, Y.-K. Lee. Mining weighted Frequent Patterns in Incremental Databases. In Proceedings of the 10th Pacific Rim Int. Conf. on Artificial Intelligence. PRICAI '08. 933--938. (Dec. 2008). Google ScholarDigital Library
- R. Agrawal, R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of Proceedings of the 20th International Conference on Very Large Data Bases. VLDB '94, 487--499. September 1994. Google ScholarDigital Library
- B.S. Jeong, Ahmed Farhan. 2010. Efficient Dynamic Weighted Frequent Pattern Mining by using a Prefix-Tree. The KIPS Transactions: Part D. Vol.17-D, No.4, pp.253--58, 2010.Google Scholar
- R. Agrawal, R. Srikant. 1994. Mining sequential patterns. Research Report RJ 9910. IBM Almaden Research Center. San Jose, California, October 1994.Google Scholar
- J. I. Kim. 2011. Real-time Sequential Pattern Mining for USN System. . In Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication. ICUIMC '12. ACM New York, NY, Article No. 36. Google ScholarDigital Library
- J. I. Kim. 2011. Real-time pattern map mining for USN application. In Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication. ICUIMC '12. ACM New York, NY, Article No. 77. Google ScholarDigital Library
Index Terms
- Dynamic weighted sequential pattern mining for USN system
Recommendations
Handling Dynamic Weights in Weighted Frequent Pattern Mining
Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. ...
From sequential pattern mining to structured pattern mining: A pattern-growth approach
AbstractSequential pattern mining is an important data mining problem with broad applications. However, it is also a challenging problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. ...
Mining weighted sequential patterns in a sequence database with a time-interval weight
Sequential pattern mining, including weighted sequential pattern mining, has been attracting much attention since it is one of the essential data mining tasks with broad applications. The weighted sequential pattern mining aims to find more interesting ...
Comments