Ming-Yen Lin
ABSTRACT
Stream data mining is to extract useful patterns or knowledge from continuous, rapid data elements in modern applications. The discovery of frequent patterns in data streams generally is constrained by the usage of bounded memory and computation time. Most algorithms for mining frequent itemsets in streaming transactions assume a fixed minimum threshold and an unchangeable time interval. The support threshold, however, should be changeable to cope with the needs of the users and the characteristics of the incoming data. In addition, allowing the specification of the interesting time period of data may enhance the discovered knowledge. Still, the number of frequent itemsets might be too large to discovering the trends or changes. Thus, maximal frequent itemsets (MFIs) with respect to a changeable support in a user specified period become a favorable objective in stream data mining. In this paper, we propose an algorithm named VIMFI for mining MFIs in a data stream, allowing an arbitrary time interval and support threshold. A bounded memory space is allocated for summarizing all the transactions. VIMFI appends transactions to the summary structure and compresses the structure when it becomes full. Corresponding transactions in the specified interval will be extracted and a mining will be performed for the desired MFIs within that interval. Experiments using both synthetic and real-world datasets demonstrate that VIMFI efficiently mines MFIs in data streams with flexible time intervals and changeable support thresholds.
- Agrawal, R. and Srikant, R. 1994. Fast algorithm for mining association rules. Proceedings of the 20th International Conference on Very Large Databases. pp. 487--499. Google Scholar
Digital Library
- Ayres, J., Flannick, J., Gehrke, J., and Yiu, T. 2002. Sequential pattern mining using a bitmap representation. Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining. pp. 429--435, July. Google ScholarDigital Library
- Bayardo Jr., R. J. 1998. Efficiently mining long patterns from databases. Proceeding of Special Interest Group on Management of Data. pp. 85--93, June. Google ScholarDigital Library
- Burdick, D., Calimlim, M., Flannick, J., Gehrke, J., and Yiu, T. 2005. MAFIA: a maximal frequent itemset algorithm. IEEE Transactions on Knowledge and Data Engineering. Vol. 17, No. 11, pp. 1490--1504, November. Google ScholarDigital Library
- Chang, J. H. and Lee, W. S. 2006. Finding recent frequent itemsets adaptively over online transactional data streams. Information Systems. Vol. 31, No. 8, pp. 849--869. Google ScholarDigital Library
- Dong, J. and Han, M. 2007. BitTableFI: an efficient mining frequent itemsets algorithm. Knowledge-Based Systems. Vol. 20, No. 4, pp. 329--335, May. Google ScholarDigital Library
- Giannella, C., Han, J., Pei, J., Yan, X., and Yu, P. S. 2002. Mining frequent patterns in data streams at multiple time granularities. Proceedings of the NSF Workshop on Next Generation Data Mining.Google Scholar
- Gouda, K. and Zaki, M. J. 2005. GenMax: an efficient algorithm for mining maximal frequent itemsets. Data Mining and Knowledge Discovery. Vol. 11, No.1, pp. 223--242, July. Google ScholarDigital Library
- Grahne, G. and Zhu, J. 2003. High performance mining of maximal frequent itemsets. Proceedings of 6th International Workshop on High Performance Data Mining. pp. 135--143.Google Scholar
- Ju, S. and Chen, C. 2008. MMFI: an effective algorithm for mining maximal frequent itemsets. Proceedings of International Symposium on Information Processing. pp. 144--148. Google ScholarDigital Library
- Koh, J. L. and Shin, S. N. 2006. An approximate approach for mining recently frequent itemsets from data streams. Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery (Poland, September). pp. 352--362. Google ScholarDigital Library
- Li, H. F. and Lee, S. Y. 2009. Approximate mining of maximal frequent itemsets in data streams with different window models. Expert Systems with Applications. Vol. 35, No. 3, pp. 781--789. Google ScholarDigital Library
- Li, H. F., Shan, M. K., and Lee, S. Y. 2008. DSM-FI: an efficient algorithm for mining frequent itemsets in data streams. Knowledge and Information Systems. Vol. 17, No. 1, pp. 79--97. Google ScholarDigital Library
- Lin, M. Y., Hsueh, S. C., and Wang, C. Y. 2008. Interactive mining of frequent patterns in a data stream of time-fading models. Proceedings of the 8th International Conference on Intelligent System Design and Applications. Vol. 1, pp. 513--518, November. Google ScholarDigital Library
- Lin, M. Y., Hsueh, S. C., and Hwang, S. K. 2008. Interactive mining of frequent itemsets over arbitrary time intervals in a data stream. CPRIT Vol. 75, Database Technologies 2008 (ADC 2008). Fekete, A. and Lin, X., Eds., pp. 15--21, January. Google ScholarDigital Library
- Lin, M. Y., Hsueh, S. C., and Hwang, S. K. 2006. Variable support mining of frequent itemsets over data streams using synopsis vectors. Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (Singapore, April). pp. 724--728. Google ScholarDigital Library
- Lin, C. H., Chiu, D. Y., Wu, Y. H., and Chen, L. P. 2005. Mining frequent itemsets from data streams with a time-sensitive sliding window. Proceedings of SIAM International Conference on Data Mining. April.Google Scholar
- Liu, G. P. 2008. Interactive Mining of Frequent Itemsets in Data Streams. Master Thesis. Feng Chia University.Google Scholar
- Manku, G. S. and Motwani, R. 2002. Approximate frequency counts over data streams. Proceedings of the 28th VLDB Conference (Hong Kong, China, August). pp. 346--357. Google ScholarDigital Library
- Wo, H. J. and Lee, W. S. 2007. EstMax: tracing maximal frequent itemsets over online data streams. Proceedings of the 7th IEEE International Conference on Data Mining. pp. 709--714, October. Google ScholarDigital Library
- Wong, C. W. and Fu, W. C. 2006. Mining top-k frequent itemsets from data streams. Data Mining and Knowledge Discovery. Vol. 13, No. 2, pp. 193--217, September. Google ScholarDigital Library
- Zou, Q., Chu, W., and Lu, B. 2002. SmartMiner: a DepthFirst algorithm guided by tail information for mining maximal frequent itemsets. Proceedings of the IEEE International Conference on Data Mining. pp. 570--590, December. Google ScholarDigital Library
Index Terms
- Interactive stream mining of maximal frequent itemsets allowing flexible time intervals and support thresholds
Recommendations
A novel approach for data stream maximal frequent itemsets mining
This paper proposes a novel algorithm AMMFI based on self-adjusting and orderly compound policy to solve the problems of existing algorithms for mining maximal frequent itemsets in a data stream. The proposed algorithm processes the data stream based on ...
Interactive mining of frequent itemsets over arbitrary time intervals in a data stream
ADC '08: Proceedings of the nineteenth conference on Australasian database - Volume 75Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches assume a fixed support threshold, a changeable threshold is more realistic, ...
Mining maximal frequent itemsets from data streams
Frequent pattern mining from data streams is an active research topic in data mining. Existing research efforts often rely on a two-phase framework to discover frequent patterns: (1) using internal data structures to store meta-patterns obtained by ...
Comments