Mining frequent closed patterns from a sliding window over data streams

Jisuanji Yanjiu yu Fazhan/Computer Research and Development

Volumn 43, Issue 10, 2006, Pages 1738-1743

  Liu, Xuejun ^a,b   Xu, Hongbing ^a   Dong, Yisheng ^a   Qian, Jiangbo ^a   Wang, Yongli ^a  

^a SOUTHEAST UNIVERSITY   (China)

^b NANJING TECH UNIVERSITY   (China)

Author keywords

Association rule; Data stream; Frequent closed item; Knowledge discovery; Sliding window

Indexed keywords

ALGORITHMS; DATA FLOW ANALYSIS; DATA PROCESSING; DATA STRUCTURES; DATABASE SYSTEMS; INTERNET; KNOWLEDGE ACQUISITION; TELECOMMUNICATION NETWORKS; TREES (MATHEMATICS);

ASSOCIATION RULE; BASIC WINDOW; DATA STREAM; DSCFI TREE; FREQUENT CLOSED ITEM; FREQUENT CLOSED PATTERN; FREQUENT PATTERN COMPLETE SET; KNOWLEDGE DISCOVERY; LATENCY FREQUENT CLOSED ITEMSETS; SLIDING WINDOW; SUBSET; UPDATING UNIT;

DATA MINING;

EID: 33845542379     PISSN: 10001239     EISSN: None     Source Type: Journal    
DOI: 10.1360/crad20061010     Document Type: Article

References (13)

1. C Giannella, J Han, J Pei, et al. Mining frequent patterns in data streams at multiple time granularities[G]. In: H Kargupta, A Joshi, K Sivakumar, et al, eds. Next Generation Data Mining. Cambridge, Mass: MIT Press, 2003
2. G S Manku, R Motwani. Approximate frequency counts over streaming data[C]. The 28th Int'l Conference on Very Large Data Bases (VLDB 2002), Hong Kong, 2002
3. Song Guojie, Wang Tengjiao, Tang Shiwei, et al. Estimation and maintenance of frequent pattern in data streams[C]. National Data Base Conference 2003, Changsha, 2003
4. Joong Hyuk Chang, Won Suk Lee. Finding recent frequent itemsets adaptively over online data streams[C]. The 9th ACM SIGKDD Int'l Conf on Knowledge Discovery and Data Mining (KDD 03), Washington, DC, 2003
5. Wei Guang Teng, Ming-Syan Chen, Philip S Yu. A regression-based temporal pattern mining scheme for data streams[C]. The Int'l Conf on Very Large Data Bases (VLDB 2003), Berlin, Germany, 2003
6. Graham Cormode, Flip Korn, S Muthukrishnan, et al. Finding hierarchical heavy hitters in data streams[C]. The Int'l Conf on Very Large Data Bases (VLDB 2003), Berlin, Germany, 2003
7. Graham Cormode, S Muthukrishnan. What's hot and What's not: Tracking most frequent items dynamically[C]. The ACM Symp on Principles of Database Systems (PODS 2003), San Diego, CA, USA, 2003
8. C Sirish, M J Franklin. Streaming queries over streaming data[C]. The 28th Int'l Conf on Very Large Data Bases, Hong Kong, 2002
9. N Pasquier, Y Bastide, R Taouil, et al. Discovering frequent closed itemsets for association rules[C]. In: Beeri C, et al, eds. Proc of the 7th Int'l Conf on Database Theory. Berlin: Springer-Verlag, 1999. 398-416
10. J Pei, J Han, R Mao. CLOSET: An efficient algorithm for mining frequent closed itemsets[C]. In: D Gunopulos, et al, eds. Proc of the 2000 ACM SIGMOD Int'l Workshop on Data Mining and Knowledge Discovery. New York: ACM Press, 2000. 21-30
11. M J Zaki, C J Hsiao. CHARM: An efficient algorithm for closed itemset mining[C]. In: R Grossman, et al, eds. Proc of the 2nd SIAM Int'l Conf on Data Mining. Arlington: SIAM, 2002. 12-28
12. Liu Junqiang, Sun Xiaoying, Zhuang Yueting, et al. Mining frequent closed patterns by adaptive pruning[J]. Journal of Software, 2004, 15(1): 94-102 (in Chinese)
13. Jiawei Han, Jianyong Wang, Ying Lu, et al. Mining top-k frequent closed patterns without minimum support[C]. IEEE Int'l Conf on Data Mining, Maebashi City, Japan, 2002